Digital navigation of chemical space for function
Lead Research Organisation:
University of Liverpool
Department Name: Chemistry
Abstract
Materials both enable the technologies we rely on today and drive advances in scientific understanding. The new scientific phenomena produced by novel materials (for example, lithium transition metal oxides) enable the creation of technologies (electric vehicles), emphasising the connection between the capability to create new materials and economic prosperity. New materials offer a route to clean growth that is essential for the future of society in the face of climate change and resource scarcity.
To harness the power of functional materials for a sustainable future, we must improve our ability to identify them. This is a daunting task, because materials are assembled from the vast and largely unknown coupled chemical and structural spaces. As a result, we are forced to work mostly by analogy with known materials to identify new ones. This necessarily incremental approach restricts the diversity of outcome from both scientific and technological perspectives. We need to be able to design materials beyond this "paradigm of analogues" if we are to exploit their potential to tackle societal challenges.
This project will transform our ability to access functional materials with unprecedented chemical and structural diversity by fusing physical and computer science. We will develop a digital discovery platform that will advance the frontier of knowledge by creating new materials classes with novel structure and bonding and tackle key application challenges, thus focussing the developed capability on well-defined targets of scientific novelty and application performance. The discovery platform will be shaped by the need to identify new materials and by the performance needed in applications. This performance is both enabled by and creates the need for the new materials classes, emphasising the interdependent nature of the project strands.
We will strengthen cutting-edge physical science (PS) capability and thinking by exploiting the extensive synergies with computer science (CS), to boost the ability of the physical scientist to navigate the space of possible materials. Computers can assimilate large databases and handle multivariate complexity in a complementary way to human experts, so we will develop models that fuse the knowledge and needs from PS with the insights from CS on how to balance precision and efficiency in the quest for promising regions in chemical space. The development of mixed techniques that use explainable symbolic AI-based automated reasoning and model construction approaches coupled with machine learning is just one example that illustrates how this opportunity goes far beyond interpolative machine learning, itself valuable as a baseline evaluation of our current knowledge.
By working collaboratively across the CS/PS interface, we can digitally explore the unknown space, informed and guided by PS expertise, to transform our ability to harvest disruptive functional materials. Only testing against the hard constraints of PS novelty and functional value will drive the discovery platform to the level needed to deliver this aim. As we are navigating uncharted space, the tools and models that we develop will be compass-like guides, rather than satellite navigation-like directors, for the expert PS team. The magnitude of the opportunity to transform materials discovery produces intense international competition with significant investments at pace from industry (e.g., Toyota Research Institute $1bn) and government (e.g., DoE $27m; a new centre at NIMS, Japan, both in 2019). Our transformative vision exploits recent UK advances in autonomous robotic researchers and artificial intelligence-guided identification of outperforming functional materials that are not based on analogues. The scale and flexibility of this PG will ensure the UK is at the forefront of this vital area.
To harness the power of functional materials for a sustainable future, we must improve our ability to identify them. This is a daunting task, because materials are assembled from the vast and largely unknown coupled chemical and structural spaces. As a result, we are forced to work mostly by analogy with known materials to identify new ones. This necessarily incremental approach restricts the diversity of outcome from both scientific and technological perspectives. We need to be able to design materials beyond this "paradigm of analogues" if we are to exploit their potential to tackle societal challenges.
This project will transform our ability to access functional materials with unprecedented chemical and structural diversity by fusing physical and computer science. We will develop a digital discovery platform that will advance the frontier of knowledge by creating new materials classes with novel structure and bonding and tackle key application challenges, thus focussing the developed capability on well-defined targets of scientific novelty and application performance. The discovery platform will be shaped by the need to identify new materials and by the performance needed in applications. This performance is both enabled by and creates the need for the new materials classes, emphasising the interdependent nature of the project strands.
We will strengthen cutting-edge physical science (PS) capability and thinking by exploiting the extensive synergies with computer science (CS), to boost the ability of the physical scientist to navigate the space of possible materials. Computers can assimilate large databases and handle multivariate complexity in a complementary way to human experts, so we will develop models that fuse the knowledge and needs from PS with the insights from CS on how to balance precision and efficiency in the quest for promising regions in chemical space. The development of mixed techniques that use explainable symbolic AI-based automated reasoning and model construction approaches coupled with machine learning is just one example that illustrates how this opportunity goes far beyond interpolative machine learning, itself valuable as a baseline evaluation of our current knowledge.
By working collaboratively across the CS/PS interface, we can digitally explore the unknown space, informed and guided by PS expertise, to transform our ability to harvest disruptive functional materials. Only testing against the hard constraints of PS novelty and functional value will drive the discovery platform to the level needed to deliver this aim. As we are navigating uncharted space, the tools and models that we develop will be compass-like guides, rather than satellite navigation-like directors, for the expert PS team. The magnitude of the opportunity to transform materials discovery produces intense international competition with significant investments at pace from industry (e.g., Toyota Research Institute $1bn) and government (e.g., DoE $27m; a new centre at NIMS, Japan, both in 2019). Our transformative vision exploits recent UK advances in autonomous robotic researchers and artificial intelligence-guided identification of outperforming functional materials that are not based on analogues. The scale and flexibility of this PG will ensure the UK is at the forefront of this vital area.
Organisations
- University of Liverpool (Lead Research Organisation)
- DIAMOND LIGHT SOURCE (Collaboration)
- NSG Nippon Sheet Glass Pilkington (Collaboration)
- UNIVERSITY OF OXFORD (Collaboration)
- Ceres Power (Collaboration)
- Science and Technologies Facilities Council (STFC) (Collaboration)
- Pilkington Glass (Collaboration)
- Unilever (United Kingdom) (Project Partner)
- University of Glasgow (Project Partner)
- IBM (United Kingdom) (Project Partner)
- Ceres Power (United Kingdom) (Project Partner)
- Max Planck Institutes (Project Partner)
- Centre for Process Innovation (Project Partner)
- Johannes Kepler University of Linz (Project Partner)
- Johnson Matthey (United Kingdom) (Project Partner)
- BAE Systems (United Kingdom) (Project Partner)
- NSG Group (UK) (Project Partner)
Publications
Butler P
(2024)
Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes
in The Journal of Physical Chemistry A
Corti L
(2023)
Disorder and Oxide Ion Diffusion Mechanism in La1.54Sr0.46Ga3O7.27 Melilite from Nuclear Magnetic Resonance.
in Journal of the American Chemical Society
Durdy S
(2023)
The Liverpool materials discovery server: a suite of computational tools for the collaborative discovery of materials
in Digital Discovery
Gao H
(2022)
A Pyrene-4,5,9,10-Tetraone-Based Covalent Organic Framework Delivers High Specific Capacity as a Li-Ion Positive Electrode.
in Journal of the American Chemical Society
Gibson Q
(2023)
Magnetic, electronic, and thermal properties of buckled kagome Fe 3 Ge 2 Sb
in Physical Review B
Gibson Q
(2022)
Single crystal growth and properties of the polar ferromagnet Mn 1.05 Bi with Kagome layers, huge magnetic anisotropy and slow spin dynamics
in Physical Review Materials
Gibson QD
(2024)
Control of Polarity in Kagome-NiAs Bismuthides.
in Angewandte Chemie (International ed. in English)
Hampson C
(2024)
A high throughput synthetic workflow for solid state synthesis of oxides
in Chemical Science
Han G
(2024)
Superionic lithium transport via multiple coordination environments defined by two-anion packing
in Science
Hargreaves C
(2023)
A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning
in npj Computational Materials
He D
(2022)
Hydrogen Isotope Separation Using a Metal-Organic Cage Built from Macrocycles.
in Angewandte Chemie (International ed. in English)
He D
(2022)
Hydrogen Isotope Separation Using a Metal-Organic Cage Built from Macrocycles
in Angewandte Chemie
Jia J
(2022)
Photoinduced inverse vulcanization.
in Nature chemistry
Kurucan M.
(2022)
Hidden 1-Counter Markov Models and How to Learn Them
in IJCAI International Joint Conference on Artificial Intelligence
Lunt AM
(2024)
Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry.
in Chemical science
Morscher A
(2022)
Control of Ionic Conductivity by Lithium Distribution in Cubic Oxide Argyrodites Li6+xP1-xSixO5Cl.
in Journal of the American Chemical Society
Newnham J
(2022)
Band Structure Engineering of Bi 4 O 4 SeCl 2 for Thermoelectric Applications
in ACS Organic & Inorganic Au
Newnham J
(2023)
Low thermal conductivity in Bi 8 CsO 8 SeX 7 (X = Cl, Br) by combining different structural motifs
in Journal of Materials Chemistry A
O'Sullivan M
(2023)
Epitaxial growth, optical and electrical conductivity of the metallic pyrochlore Bi2Ru2O7 on Y-stabilized ZrO2 substrate
in APL Materials
Quayle JJ
(2023)
A proxy for oxygen storage capacity from high-throughput screening and automated data analysis.
in Chemical science
Shields C
(2023)
Experimental Confirmation of a Predicted Porous Hydrogen-Bonded Organic Framework
in Angewandte Chemie International Edition
Shields C
(2023)
Experimental Confirmation of a Predicted Porous Hydrogen-Bonded Organic Framework
in Angewandte Chemie
Surta T
(2024)
Separation of K+ and Bi3+ displacements in a Pb-free, monoclinic piezoelectric at the morphotropic phase boundary
in Acta Materialia
Vasylenko A
(2023)
Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties
in npj Computational Materials
Wright M
(2024)
Accessing Mg-Ion Storage in V2PS10 via Combined Cationic-Anionic Redox with Selective Bond Cleavage
in Angewandte Chemie International Edition
Zamaraeva E.
Reinforcement learning in crystal structure prediction
in Digital Discovery
Zhu Q
(2022)
Analogy Powered by Prediction and Structural Invariants: Computationally Led Discovery of a Mesoporous Hydrogen-Bonded Organic Cage Crystal.
in Journal of the American Chemical Society
Zhu Q
(2023)
Soft Hydrogen-Bonded Organic Frameworks Constructed Using a Flexible Organic Cage Hinge.
in Journal of the American Chemical Society
Description | The project team have demonstrated how digital and automated workflows spanning physical model-based computation, machine learning and autonomous robotics lead to outperforming functional materials. The development of explainable artificial intelligence approaches to further diversify these workflows is underway, together with automated tools to guide experimental selection based on uncertainty-quantified experimental data assessment when exploring complex chemical spaces. Early exemplification of the outputs of these tools include the computationally-guided discovery of a porous material based on a predicted supramolecular assembly and identified with robotic synthesis. A complementary example is the combination of machine learning and crystal structure prediction to support experimental researchers in their discovery of a new family of lithium solid electrolytes that demonstrate a new connection between structure and superionic transport. This work on solid electrolytes connects to the development of new machine learning models for the prediction of properties, part of the growing use of machine learning for example in potentials as part of large-scale assessments of crystal structure in complex organic materials. As the project focusses on the development of new tools and capabilities integrated with the synthesis of new materials, we have reported and evaluated new classes of structure such as polar Kagome nets of interest for topological materials, and new mechanisms for magnesium insertion into solids of relevance to magnesium ion batteries. |
Exploitation Route | The design and discovery of materials underpins advances in technology and scientific understanding. The new workflows and capabilities that combine digital tools with automated and autonomous robotics and serial experimentation open new directions for materials discovery. These directions will benefit all industries relying on materials, and the associated capabilities and materials will benefit academic and industrial researchers in materials-using industries. Specific user interactions are detailed in the Narrative Impact section. |
Sectors | Aerospace Defence and Marine Chemicals Digital/Communication/Information Technologies (including Software) Electronics Energy Manufacturing including Industrial Biotechology Pharmaceuticals and Medical Biotechnology |
Description | The project interacts closely with industrial partners spanning energy technologies, coatings and sustainable manufacturing, with activities from joint research to the transfer of know-how and technology, for example in separations and in coatings. The transfer of project capability in machine learning and in automated workflow design to two partners is reflected in visits from their senior technical teams to maximise the working-level outcomes of these interactions. The impact of the work on materials discovery for functional coatings is demonstrated by the signing of a Memorandum of Understanding in 2023 between the University of Liverpool and NSG Pilkington, with the signing of the statement of intent between the University of Liverpool and Ceres Power in 2024 reflecting that of work on ion transport. The recently announced AI for Chemistry Hub has core partners Imperial College, Liverpool and Southampton represented by project investigators, together with Cambridge, and drew effectively on external partnerships within the project. The industrial dissemination of project outcomes in terms of materials and capability is driven by the Knowledge Centre for Materials Chemistry (KCMC), and project advances were presented to the Industrial Steering Group (ISG) of KCMC in November 2023, where the impact on net zero, the topic of the ISG meeting, was emphasised. The academic and applied impact of the projects work was recognised by the receipt of the Eni Award Energy Frontiers 2023 for the digital design and discovery of next-generation energy materials from the President of Italy. Work in automated and digital materials chemistry at the University of Liverpool was recognised with the 2024 Queens Anniversary Prize for the Department of Chemistry at the University of Liverpool. |
First Year Of Impact | 2023 |
Sector | Chemicals,Construction,Digital/Communication/Information Technologies (including Software),Energy,Environment |
Impact Types | Economic |
Description | Policy briefing, Net zero aviation fuels: resource requirements and environmental impacts |
Geographic Reach | National |
Policy Influence Type | Implementation circular/rapid advice/letter to e.g. Ministry of Health |
Impact | The report looked at four alternative fuels: hydrogen, ammonia, synthetic fuels (efuels) and biofuels, and examines each option against: equivalent resources that would be required for that option to replace fossil jet fuel; life cycle analysis and non-CO2 environmental impacts; likely costs; and modification or replacements needed to implement the option. It is evident that all alternative fuel options have advantages and challenges and there is no single simple answer to decarbonising aviation. The main conclusions of the report are: - Availability and accessibility of sustainable feedstock for all options is a key challenge. - Further R&D will be needed in the development of the efficient production, storage and use of green hydrogen, ammonia and efuels. - Further development of LCAs of all alternative aviation fuels is required which will be critical in clarifying emissions across the entire cycle and highlighting key mitigation opportunities. - R&D is required to understand and mitigate the non-CO2 climate impacts of all the alternative fuel options. - A holistic approach with regards to alternative fuel and engine and airframe development will be needed. - Considerations will have to be made on handling multiple technologies both in the airport and aircraft. - Staff and crew will need specialised training on handling alternative fuels, and the public will need to be informed about the relevant safety concerns within the airport and aircraft. |
URL | https://royalsociety.org/topics-policy/projects/low-carbon-energy-programme/net-zero-aviation-fuels/ |
Title | A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning |
Description | The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15-35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | Enhancement of machine learning models for prediction of Li ion conductivity in solid state materials. |
Title | CCDC 2151938: Experimental Crystal Structure Determination |
Description | Related Article: Donglin He, Linda Zhang, Tao Liu, Rob Clowes, Marc A. Little, Ming Liu, Michael Hirscher, Andrew I. Cooper|2022|Angew.Chem.,Int.Ed.|61|e202202450|doi:10.1002/anie.202202450 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2b78c7&sid=DataCite |
Title | CCDC 2151939: Experimental Crystal Structure Determination |
Description | Related Article: Donglin He, Linda Zhang, Tao Liu, Rob Clowes, Marc A. Little, Ming Liu, Michael Hirscher, Andrew I. Cooper|2022|Angew.Chem.,Int.Ed.|61|e202202450|doi:10.1002/anie.202202450 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2b78d8&sid=DataCite |
Title | CCDC 2151940: Experimental Crystal Structure Determination |
Description | Related Article: Donglin He, Linda Zhang, Tao Liu, Rob Clowes, Marc A. Little, Ming Liu, Michael Hirscher, Andrew I. Cooper|2022|Angew.Chem.,Int.Ed.|61|e202202450|doi:10.1002/anie.202202450 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2b78f9&sid=DataCite |
Title | CCDC 2157172: Experimental Crystal Structure Determination |
Description | Related Article: Qiang Zhu, Jay Johal, Daniel E. Widdowson, Zhongfu Pang, Boyu Li, Christopher M. Kane, Vitaliy Kurlin, Graeme M. Day, Marc A. Little, Andrew I. Cooper|2022|J.Am.Chem.Soc.|144|9893|doi:10.1021/jacs.2c02653 |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2bdq6p&sid=DataCite |
Title | CCDC 2157173: Experimental Crystal Structure Determination |
Description | Related Article: Qiang Zhu, Jay Johal, Daniel E. Widdowson, Zhongfu Pang, Boyu Li, Christopher M. Kane, Vitaliy Kurlin, Graeme M. Day, Marc A. Little, Andrew I. Cooper|2022|J.Am.Chem.Soc.|144|9893|doi:10.1021/jacs.2c02653 |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2bdq7q&sid=DataCite |
Title | CCDC 2241787: Experimental Crystal Structure Determination |
Description | Related Article: Caitlin E. Shields, Xue Wang, Thomas Fellowes, Rob Clowes, Linjiang Chen, Graeme M. Day, Anna G. Slater, John W. Ward, Marc A. Little, Andrew I. Cooper|2023|Angew.Chem.,Int.Ed.||e202303167|doi:10.1002/anie.202303167 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2f7rq5&sid=DataCite |
Title | CCDC 2241788: Experimental Crystal Structure Determination |
Description | Related Article: Caitlin E. Shields, Xue Wang, Thomas Fellowes, Rob Clowes, Linjiang Chen, Graeme M. Day, Anna G. Slater, John W. Ward, Marc A. Little, Andrew I. Cooper|2023|Angew.Chem.,Int.Ed.||e202303167|doi:10.1002/anie.202303167 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2f7rr6&sid=DataCite |
Title | CCDC 2241789: Experimental Crystal Structure Determination |
Description | Related Article: Caitlin E. Shields, Xue Wang, Thomas Fellowes, Rob Clowes, Linjiang Chen, Graeme M. Day, Anna G. Slater, John W. Ward, Marc A. Little, Andrew I. Cooper|2023|Angew.Chem.,Int.Ed.||e202303167|doi:10.1002/anie.202303167 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2f7rs7&sid=DataCite |
Title | CCDC 2253690: Experimental Crystal Structure Determination |
Description | Related Article: Qiang Zhu, Lei Wei, Chengxi Zhao, Hang Qu, Bowen Liu, Thomas Fellowes, Siyuan Yang, Alexandra Longcake, Michael J. Hall, Michael R. Probert, Yingbo Zhao, Andrew I. Cooper, Marc A. Little|2023|J.Am.Chem.Soc.|145|23352|doi:10.1021/jacs.3c09246 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2fn4py&sid=DataCite |
Title | CCDC 2253691: Experimental Crystal Structure Determination |
Description | Related Article: Qiang Zhu, Lei Wei, Chengxi Zhao, Hang Qu, Bowen Liu, Thomas Fellowes, Siyuan Yang, Alexandra Longcake, Michael J. Hall, Michael R. Probert, Yingbo Zhao, Andrew I. Cooper, Marc A. Little|2023|J.Am.Chem.Soc.|145|23352|doi:10.1021/jacs.3c09246 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2fn4qz&sid=DataCite |
Title | CCDC 2253692: Experimental Crystal Structure Determination |
Description | Related Article: Qiang Zhu, Lei Wei, Chengxi Zhao, Hang Qu, Bowen Liu, Thomas Fellowes, Siyuan Yang, Alexandra Longcake, Michael J. Hall, Michael R. Probert, Yingbo Zhao, Andrew I. Cooper, Marc A. Little|2023|J.Am.Chem.Soc.|145|23352|doi:10.1021/jacs.3c09246 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2fn4r0&sid=DataCite |
Title | CCDC 2253693: Experimental Crystal Structure Determination |
Description | Related Article: Qiang Zhu, Lei Wei, Chengxi Zhao, Hang Qu, Bowen Liu, Thomas Fellowes, Siyuan Yang, Alexandra Longcake, Michael J. Hall, Michael R. Probert, Yingbo Zhao, Andrew I. Cooper, Marc A. Little|2023|J.Am.Chem.Soc.|145|23352|doi:10.1021/jacs.3c09246 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2fn4s1&sid=DataCite |
Title | CCDC 2253694: Experimental Crystal Structure Determination |
Description | Related Article: Qiang Zhu, Lei Wei, Chengxi Zhao, Hang Qu, Bowen Liu, Thomas Fellowes, Siyuan Yang, Alexandra Longcake, Michael J. Hall, Michael R. Probert, Yingbo Zhao, Andrew I. Cooper, Marc A. Little|2023|J.Am.Chem.Soc.|145|23352|doi:10.1021/jacs.3c09246 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2fn4t2&sid=DataCite |
Title | CCDC 2290410: Experimental Crystal Structure Determination |
Description | Related Article: Qiang Zhu, Lei Wei, Chengxi Zhao, Hang Qu, Bowen Liu, Thomas Fellowes, Siyuan Yang, Alexandra Longcake, Michael J. Hall, Michael R. Probert, Yingbo Zhao, Andrew I. Cooper, Marc A. Little|2023|J.Am.Chem.Soc.|145|23352|doi:10.1021/jacs.3c09246 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2gwc6y&sid=DataCite |
Title | CCDC 2290411: Experimental Crystal Structure Determination |
Description | Related Article: Qiang Zhu, Lei Wei, Chengxi Zhao, Hang Qu, Bowen Liu, Thomas Fellowes, Siyuan Yang, Alexandra Longcake, Michael J. Hall, Michael R. Probert, Yingbo Zhao, Andrew I. Cooper, Marc A. Little|2023|J.Am.Chem.Soc.|145|23352|doi:10.1021/jacs.3c09246 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc2gwc7z&sid=DataCite |
Title | CSD 2246936: Experimental Crystal Structure Determination |
Description | Related Article: Jon A. Newnham, Quinn D. Gibson, T. Wesley Surta, Alexandra Morscher, Troy D. Manning, Luke M. Daniels, John B. Claridge, M. J. Rosseinsky|2023|J.Mater.Chem.A|11|15739|doi:10.1039/D3TA01630G |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.25505/fiz.icsd.cc2ff3tt&sid=DataCite |
Title | Composition based ML predictions of Li-electrolyte conductivity |
Description | A machine learning model to predict Lithium conductivity from composition. More data available on https://github.com/lrcfmd/LiIonML. |
Type Of Material | Computer model/algorithm |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | Easy to use platform of tools for materials discovery. |
URL | https://lmds.liverpool.ac.uk/ionics_ml |
Title | ElM2D |
Description | A tool to evaluate similarity within a given list of compositions. |
Type Of Material | Computer model/algorithm |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | Easy to use platform of computational tools. |
URL | https://lmds.liverpool.ac.uk/ElM2D |
Title | ElMTree |
Description | A tool to identify chemical compositions reported in the literature that are most similar to the target composition. |
Type Of Material | Computer model/algorithm |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | Easy to use platform of computational tools. |
URL | https://lmds.liverpool.ac.uk/ElMTree |
Title | Phase Field Ranking |
Description | An ML model that affords numerical assessment of the phase fields based on the knowledge extracted with unsupervised learning of the Inorganic Crystal Structure Database (ICSD) of materials, and ranks the unexplored chemistry by similarity with the materials found in ICSD. |
Type Of Material | Computer model/algorithm |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | The model affords numerical assesment of the phase fields based on the knowledge exctracted with unsupervised learning of the Inorganic Crystal Structure Database (ICSD) of materials, and ranks the unexplored chemistry by similarity with the materials found in ICSD. |
URL | https://github.com/lrcfmd/PhaseFieldRanking |
Title | PhaseBO |
Description | A computational tool for Bayesian optimisation of the chemical phase fields for accelerated discovery of stable compositions in inorganic materials. |
Type Of Material | Computer model/algorithm |
Year Produced | 2024 |
Provided To Others? | Yes |
Impact | The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation. |
URL | https://github.com/lrcfmd/PhaseBO |
Title | Prediction of MOF porosity |
Description | A machine learning model to predict porosity of a Metal-Organic Framework for a given metal and linker combination. |
Type Of Material | Computer model/algorithm |
Year Produced | 2022 |
Provided To Others? | Yes |
Impact | Both the dataset and the predictive models are available to download and offer simple guidance in prioritization of the choice of the components for exploratory MOF synthesis for separation and catalysis based on guest accessibility considerations. |
URL | https://lmds.liverpool.ac.uk/mof_ml |
Title | Site-Net |
Description | Crystal structure representation with global self-attention and real-space supercells to capture long-range interactions in crystal structures |
Type Of Material | Computer model/algorithm |
Year Produced | 2022 |
Provided To Others? | Yes |
Impact | Site-Net is a transformer architecture that models the periodic crystal structures of inorganic materials as a labelled point set of atoms and relies entirely on global self-attention and geometric information to guide learning. |
URL | https://github.com/lrcfmd/Site-Net |
Title | Thermoelectrics thermal conductivity prediction tool |
Description | A machine learning model to predict thermal conductivity from composition. |
Type Of Material | Computer model/algorithm |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | Easy to use platform tool available on Liverpool materials discovery server. |
URL | https://lmds.liverpool.ac.uk/thermal_conductivity |
Title | veltiCRYS |
Description | A collection of modules with functions useful for geometry optimization of ionic structures. |
Type Of Material | Computer model/algorithm |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | velti (from ße?t?st?p???s? meaning optimization in Greek) for CRYStals is a collection of modules with functions useful for geometry optimization of ionic structures. |
URL | https://github.com/lrcfmd/veltiCRYS |
Description | Ceres Power Memorandum of Understanding |
Organisation | Ceres Power |
Country | United Kingdom |
Sector | Private |
PI Contribution | This MoU formalises the on-going collaboration between the University of Liverpool and Ceres Power. Liverpool have provided expertise and IP (through IP transfer) to Ceres power as well as access to equipment and methodologies. |
Collaborator Contribution | This MoU formalises the on-going collaboration between the University of Liverpool and Ceres Power. Ceres Power have previously purchased Liverpool IP relating to new solid oxide fuel cell cathode materials and provided their expertise in SOFC materials to accelerate our discovery activities in this area. |
Impact | Previous IP puchased by Ceres Power This is a multidisciplinary collaboration covering materials chemistry and physics, computational chemistry and computer science |
Start Year | 2024 |
Description | Collaboration with NSG Group (borosilicate coatings) |
Organisation | Pilkington Glass |
Department | Pilkington Technology Centre |
Country | United Kingdom |
Sector | Private |
PI Contribution | We have hosted a NSG scientist in our labs to assist with NSG's development of borosilicate glass coatings we have provided facilities and technical advice on synthesis and analysis. |
Collaborator Contribution | The NSG scientist has worked on our laboratories bringing a new perspective on research to the team and discussions on thin film preparation using solution based routes which have been useful on other projects in the group |
Impact | No direct outputs so far. |
Start Year | 2021 |
Description | Collaboration with Prof. Alan Chadwick and B18 beamline Diamond Light Source |
Organisation | Diamond Light Source |
Country | United Kingdom |
Sector | Private |
PI Contribution | Provided cycled battery cathode samples for data collection, and analysing data. |
Collaborator Contribution | Access route to B18 XAS beamline, advice on sample preparation, data collection and data analysis. |
Impact | None yet. |
Start Year | 2022 |
Description | Computational Prediction of New Transparent Conducting Materials |
Organisation | Pilkington Glass |
Department | Pilkington Technology Centre |
Country | United Kingdom |
Sector | Private |
PI Contribution | Computational structure prediction methods are being used to discover new transparent conducting materials for use as coatings on glass for optoelectronic and energy saving applications. The partnership is a three-way collaboration between our research team, NSG/Pilkington and the Hartree Centre. The Liverpool-NSG interaction is funded through an Impact Accelerator Account - Secondment award with significant cash contribution from NSG. Access to the Hartree supercomputers is funding through a Business for Innovators award. A PDRA employed by Liverpool and seconded to NSG's technical centres in the UK, US and Japan, will use computational structure prediction methods developed in the award |
Collaborator Contribution | NSG/Pilkington have invested £150,000 in cash to this project, £75,000 towards an Impact Accelerator Account - Secondment for a PDRA to perform the research and £75,000 towards a Business of Innovators award from the Science and Technologies Facilities Council. They have also committed £110,000 of in-kind support through 2.5 days per week of personnel time (£50,000 p.a.) and £10,000 in travel and time to allow the researcher to visit the NSG US and Japanese technical centres. In January 2020 NSG have invested a further £75,000 to fund a PDRA and PhD to work on synthesis of new materials predicted from the computational project. |
Impact | NSG have a long term (>5 year) vision for the collaboration, extending the research beyond transparent conductors into other materials of interest to the business. As a direct result of this collaboration NSG have committed to a 0.5 funded PhD student to compliment the PDRA. As of January 2020 NSG have invested a further £70,000 to partly fund a PDRA (starting February 2020) and a PhD student (starting September 2020) to begin synthesis of the transparent conducting materials predicted from the computational study. Synthesis work has begun and results are being generated. |
Start Year | 2018 |
Description | Computational Prediction of New Transparent Conducting Materials |
Organisation | Science and Technologies Facilities Council (STFC) |
Department | Hartree Centre |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Computational structure prediction methods are being used to discover new transparent conducting materials for use as coatings on glass for optoelectronic and energy saving applications. The partnership is a three-way collaboration between our research team, NSG/Pilkington and the Hartree Centre. The Liverpool-NSG interaction is funded through an Impact Accelerator Account - Secondment award with significant cash contribution from NSG. Access to the Hartree supercomputers is funding through a Business for Innovators award. A PDRA employed by Liverpool and seconded to NSG's technical centres in the UK, US and Japan, will use computational structure prediction methods developed in the award |
Collaborator Contribution | NSG/Pilkington have invested £150,000 in cash to this project, £75,000 towards an Impact Accelerator Account - Secondment for a PDRA to perform the research and £75,000 towards a Business of Innovators award from the Science and Technologies Facilities Council. They have also committed £110,000 of in-kind support through 2.5 days per week of personnel time (£50,000 p.a.) and £10,000 in travel and time to allow the researcher to visit the NSG US and Japanese technical centres. In January 2020 NSG have invested a further £75,000 to fund a PDRA and PhD to work on synthesis of new materials predicted from the computational project. |
Impact | NSG have a long term (>5 year) vision for the collaboration, extending the research beyond transparent conductors into other materials of interest to the business. As a direct result of this collaboration NSG have committed to a 0.5 funded PhD student to compliment the PDRA. As of January 2020 NSG have invested a further £70,000 to partly fund a PDRA (starting February 2020) and a PhD student (starting September 2020) to begin synthesis of the transparent conducting materials predicted from the computational study. Synthesis work has begun and results are being generated. |
Start Year | 2018 |
Description | IP transfer project NSG |
Organisation | Pilkington Glass |
Department | Pilkington Technology Centre |
Country | United Kingdom |
Sector | Private |
PI Contribution | In a continuation of the previous HEIF and IAA funding developing technology discovered in the programme grant, the post-doctoral researcher has been employed by NSG full-time to develop the UoL IP in-house. The researcher spends ~50% of time at Liverpool, 50% at NSG. |
Collaborator Contribution | The post-doctoral researcher has been employed by NSG full-time to develop the UoL IP in-house. The researcher spends ~50% of time at Liverpool, 50% at NSG. |
Impact | The post-doctoral researcher is now employed at NSG (see relevant entry in Next Destinations). This is a multi-disciplinary collaboration involving chemists, physicists, materials scientist. |
Start Year | 2024 |
Description | NSG IP transfer |
Organisation | NSG Nippon Sheet Glass Pilkington |
Country | Japan |
Sector | Private |
PI Contribution | A project has been started to transfer IP and know-how to NSG Group. Funding was obtained through a HEIF award and from NSG for a 1-year post-doctoral researcher to transfer and develop new transparent conducting coatings to large area technologies used by NSG. Subsequent IAA funding was obtained to extend the IP transfer project for a further year. During both project the post-doctoral researcher spent significant time using facilities at NSG's Techicanl Centre at Lathom, Lancashire. |
Collaborator Contribution | NSG will use their large scale coating technology to deposit transparent conducting coating systems developed by the Liverpool team. The NSG team is led from the UK with the deposition work taking place in Japan. NSG are funding 50% of the PDRA and in-kind support of time and facilities. |
Impact | Funding was obtained from Liverpool HEIF award (£35,000), match funding is provided by NSG for PDRA salary. |
Start Year | 2021 |
Description | NSG Memorandum of Understanding |
Organisation | Pilkington Glass |
Department | Pilkington Technology Centre |
Country | United Kingdom |
Sector | Private |
PI Contribution | This MoU formalises the collaboration between the University of Liverpool and NSG. Liverpool, with NSG, have jointly funded at least 11 PhD studentships over the previous 5-6 years related to activities in coatings on glass, including computational and experimental projects and are now expanding into other areas of interest for NSG e.g. zero thermal expansion materials. All the resaerchers have access to the equipment, facilities and expertise available in the group and university and the training opportunities provided by the group and univeristy. |
Collaborator Contribution | This MoU formalises the collaboration between the University of Liverpool and NSG. Liverpool, with NSG, have jointly funded at least 11 PhD studentships over the previous 5-6 years related to activities in coatings on glass, including computational and experimental projects and are now expanding into other areas of interest for NSG e.g. zero thermal expansion materials. All university based researchers are assigned an industrial supervisor. Projects are given access to relevant data, either laboratory or production data depending on the project. All researchers are provided access to the expertise facilities at the NSG technical centres. |
Impact | This is a multi-disciplinary collaboration covering experimental chemistry and physics, computational chemistry and computer science. Two patent applications have been filed relating to new materials discovered through the collaboration. At least 4 researchers from the collaboration have subsequently been employed by NSG |
Start Year | 2023 |
Description | NSG Zero thermal expansion materials |
Organisation | Pilkington Glass |
Department | Pilkington Technology Centre |
Country | United Kingdom |
Sector | Private |
PI Contribution | Initial discussions on zero thermal expansion materials to lead to collaborative project |
Collaborator Contribution | Initial discussions on zero thermal expansion materials to lead to collaborative project |
Impact | No outputs. Will be a multidisciplinary project involving computer science and experimental chemistry |
Start Year | 2023 |
Description | New collaboration with Oxford PV research group for device preparation and measurement |
Organisation | University of Oxford |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Preparation of new materials for photovoltaic and solar absorber applications. This involved crystal growth, powder synthesis, characterisation of the new materials. |
Collaborator Contribution | Preparation of devices and measurement of devices based on new materials provided by Liverpool team. |
Impact | Collaboration between inorganic chemists and device physicists. |
Start Year | 2018 |
Title | Compound |
Description | New materials in the Ba-Mo4+-P-O phase field |
IP Reference | US 18/485,361 |
Protection | Patent / Patent application |
Year Protection Granted | 2023 |
Licensed | Yes |
Impact | Patent application filed by NSG Group on outputs from collaboration within EP/V026887 |
Title | Compound |
Description | New materials in the Ba-Mo5+-P-O phase field; patent application filed by NSG Group on results from collaboration |
IP Reference | US 18/485,372 |
Protection | Patent / Patent application |
Year Protection Granted | 2023 |
Licensed | Yes |
Impact | New materials in the Ba-Mo5+-P-O phase field; patent application filed by NSG Group on results from collaboration |
Title | Fast ion conducting Li material |
Description | New concept for the structural requirements for fast Li ion conductors and an example compound |
IP Reference | GB2302541.4 |
Protection | Patent / Patent application |
Year Protection Granted | 2023 |
Licensed | No |
Impact | Invention described in the patent application was subsequently published in Science 2024 |
Title | FUSE-RL |
Description | FUSE-RL that uses Reinforcement Learning to accelerate calculations. |
Type Of Technology | New/Improved Technique/Technology |
Year Produced | 2023 |
Open Source License? | Yes |
Impact | Acceleration of calculations. |
Title | FUSE2 |
Description | FUSE2 that uses ML-generated structures and automated generation of units. Base model of software: The set of Crystal Structure Prediction tools for inorganic solids based on the Flexible Unit Structure Engine (FUSE). |
Type Of Technology | New/Improved Technique/Technology |
Year Produced | 2023 |
Open Source License? | Yes |
Impact | The use of a generative machine learning model to produce the starting population of crystal structures for a heuristic algorithm and discuss the benefits, demonstrating the method on eight known compounds with reported crystal structures and three hypothetical compounds. |
URL | https://chemrxiv.org/engage/chemrxiv/article-details/658065cb9138d23161f68ac6 |
Title | Integer programming for crystal structure prediction |
Description | IPCSP software package that uses Integer Programming for Crystal Structure Prediction |
Type Of Technology | Software |
Year Produced | 2023 |
Open Source License? | Yes |
Impact | The task of finding a periodic allocation of atoms on a grid in space that minimises their pairwise interaction energy can be encoded as an integer program. Such integer program can be solved either using advanced solvers to obtain provably optimal solutions or using quantum annealers (or any other Ising machine) for a potential speed-up or energy efficiency. Subsequently, solutions of the periodic lattice allocation problems can be used to predict crystal structures of materials or perform other investigations of the potential energy surfaces. |
URL | https://www.nature.com/articles/s41586-023-06071-y#citeas |
Description | AM poster RSC Solid State Chemistry Group Christmas Meeting 2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Alexandra Morscher RSC SSCG Christmas Meeting 2023 at University of Edinburgh poster presentation: Stabilising Li+ Disorder in Hexagonal Argyrodites via Framework Lattice Entropy |
Year(s) Of Engagement Activity | 2023 |
Description | BP Applied Sciences visit |
Form Of Engagement Activity | Participation in an open day or visit at my research institution |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Members of the Applied Sciences - bp Innovation & Engineering team visited MIF and SIRE to discuss potential collaborations |
Year(s) Of Engagement Activity | 2024 |
Description | CH talk RSC Solid State Chemistry Group Christmas Meeting 2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Cara Hawkins RSC SSCG Christmas Meeting 2023 at University of Edinburgh oral presentation: Synthesis, structure & properties of CuBiSeCl2: a new chalcohalide material with low thermal conductivity |
Year(s) Of Engagement Activity | 2023 |
Description | Cheetham Lecture at UC Santa Barbara |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | The Cheetham Lecturer is an annual award honoring a scientist who has made significant contributions to materials research with industrial applications.The Cheetham Lecturer presents their work at the annual Materials Research Outreach Symposium (MROP) held at UCSB. https://www.mrl.ucsb.edu/industry/materials-research-outreach-program |
Year(s) Of Engagement Activity | 2023 |
URL | https://www.mrl.ucsb.edu/people/cheetham-lecturer |
Description | FO Conference presentation (NeurIPS) |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Federico Ottomano presented at NeurIPs, New Orleans Dec 2023: Enhancing Extrapolation in Materials Science through Contrastive Learning of Chemical Compositions |
Year(s) Of Engagement Activity | 2023 |
URL | https://nips.cc/virtual/2023/78468 |
Description | FO presentation to NSG |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Federico Ottomano presented to NSG US Technical Centre Toledo, OH, Dec 2023: |
Year(s) Of Engagement Activity | 2023 |
Description | HL talk RSC Solid State Chemistry Group Christmas Meeting 2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Hai Lin RSC SSCG Christmas Meeting 2023 at University of Edinburgh oral presentation Control of polarity in kagome-NiAs bismuthides |
Year(s) Of Engagement Activity | 2023 |
Description | Hosted conference on Structural Novelty and Complexity in Materials |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Join us at the University of Liverpool for a one day conference to discuss novel and complex materials. The conference promises to be a great forum for material scientists to learn from experts in the field, while networking and building collaborations within the community. Speakers: Andrew Goodwin, University of Oxford Bo Brummerstedt Iversen, Aarhus University Ella Schmidt, Universität Bremen Karen Johnston, Durham University Marwin Segler, Microsoft Research Michael Mastalerz, Heidelberg University Robert Palgrave, University College London Roger Johnson, University College London |
Year(s) Of Engagement Activity | 2024 |
URL | https://www.eventbrite.com/e/structural-novelty-and-complexity-in-materials-tickets-750652962217?aff... |
Description | Hosting a visit of Keir Starmer at the Materials innovation Factory |
Form Of Engagement Activity | Participation in an open day or visit at my research institution |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Supporters |
Results and Impact | Event to promote the Materials innovation factory and it's activity in chemistry automation and robotics to the government and highlight research activity in he Liverpool City Region. |
Year(s) Of Engagement Activity | 2023 |
URL | https://news.liverpool.ac.uk/2023/10/12/keir-starmer-and-rachel-reeves-visit-materials-innovation-fa... |
Description | Invitation to participate in College de France seminar series on solid-state batteries |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Gave a talk entitled 'Digital Routes to Inorganic Materials - A New Pathway for Ion Transport in Solids |
Year(s) Of Engagement Activity | 2024 |
URL | https://www.college-de-france.fr/fr/agenda/cours/la-batterie-tout-solide-entre-ideal-et-pragmatisme |
Description | Invited presentation at the inauguration of the new CRISMAT laboratory, Laboratoire de Cristallographie et Sciences des Matériaux |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Invited presentation at the inauguration of the new CRISMAT laboratory, Laboratoire de Cristallographie et Sciences des Matériaux, Caen - 4th July 2023 |
Year(s) Of Engagement Activity | 2023 |
Description | Invited talk at GRC: Computational Materials Science and Engineering, USA, August 2022 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Invited talk given to a community of computational chemists and material scientists. |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.grc.org/computational-materials-science-and-engineering-conference/2022/ |
Description | Invited talk at Materials Research Society conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Gave invited talk titled "Defects and design in energy materials" at Materials Research Society in Boston on 29 November 2022 |
Year(s) Of Engagement Activity | 2022 |
Description | Invited talk at Materials Research Society conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Design and Discovery of Multiple Anion Functional Materials - Synthesis, Structure, Computation and Machine Learning Materials Research Society in Hawaii, USA |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.mrs.org/meetings-events/spring-meetings-exhibits/past-spring-meetings/2022-mrs-spring-me... |
Description | Johnson Matthey Academic Conference 2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Matthew Wright presented at JMAC 2023: Accessing Multivalent Ion Storage in V2PS10 via Combined Cationic-Anionic Redox with Selective Bond Cleavage |
Year(s) Of Engagement Activity | 2023 |
Description | Luke Daniels Warwick University Chemistry Department Invited Seminar |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | Luke Daniels presented in invited seminar in January 2024 at the University of Warwick. Title: From intermetallics to ion conductors |
Year(s) Of Engagement Activity | 2024 |
Description | MD poster RSC Solid State Chemistry Group Christmas Meeting 2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Moinak Dutta RSC SSCG Christmas Meeting 2023 at University of Edinburgh poster presentation: Structure and magnetic properties of K intercalated coronene |
Year(s) Of Engagement Activity | 2023 |
Description | MD poster RSC Solid State Chemistry Group Christmas Meeting 2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Matthew Dyer RSC SSCG Christmas Meeting 2023 at University of Edinburgh poster presentation: The Liverpool materials discovery server: a suite of computational tools for the collaborative discovery of materials |
Year(s) Of Engagement Activity | 2023 |
Description | MW poster RSC Solid State Chemistry Group Christmas Meeting 2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Matthew Wright RSC SSCG Christmas Meeting 2023 at University of Edinburgh poster presentation: Accessing Multivalent Ion Storage in V2PS10 via Combined Cationic-Anionic Redox with Selective Bond Cleavage |
Year(s) Of Engagement Activity | 2023 |
Description | NH poster RSC Solid State Chemistry Group Christmas Meeting 2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Nataliia Hulai RSC SSCG Christmas Meeting 2023 at University of Edinburgh poster presentation: Navigation through High-Dimensional Chemical Space: Discovery of New Ba-Y-Si-O Materials |
Year(s) Of Engagement Activity | 2023 |
Description | NSG Incubator team |
Form Of Engagement Activity | Participation in an open day or visit at my research institution |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Members of the NSG Incubator team visited MIF and DIF (14/12/2023) to explore new opportunties for collaboration |
Year(s) Of Engagement Activity | 2023 |
Description | NSG symposium March 2024 |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Annual symposium hosted by NSG for sponsored PhD students to showcase their work and provide networking opportunities for new collaborations. |
Year(s) Of Engagement Activity | 2024 |
Description | NSG-UoL Symposium |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Industry/Business |
Results and Impact | One-day symposium to present the current collaborative activities between NSG and UoL to the broader NSG Research and Development community. Presentations from the lead academics and PhD students allowed discussion and new collaborative ideas to be generated. Over 50 researchers from various teams at NSG attended. New jointly supervised PhD projects, to start October 2023, have directly resulted from the symposium. The new projects are in business areas beyond those involved in the initial projects, illustrating the impact of the collaboration on the wider NSG Group business. |
Year(s) Of Engagement Activity | 2022 |
Description | Original Ideas podcast: How is AI being used to transform research |
Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | In this podcast, Gavin Freeborn is joined by Professors Katie Atkinson and Andy Cooper, and early career researchers Dr Jack Mumford and Dr Gabriella Pizzuto to discuss the following topics. What impact is AI making in academic circles? How is AI being used now to transform research across Chemistry and Computer Science? What are the hopes for the future? |
Year(s) Of Engagement Activity | 2024 |
URL | https://www.youtube.com/watch?v=QRcDAR7MfRU |
Description | Plenary Lecture, MASC 2022, University of Nottingham, UK, Dec. 20th 2022 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | The plenary lecture presented research findings, innovative methodologies, and discoveries that promise to reshape the landscape of the chemistry field. The audience was captivated by the impactful research, sparking discussions and excitement within the academic community. |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.asynt.com/events/masc-2022/ |
Description | Plenary talk at World Congress on Oxidation Catalysis |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Gave plenary lecture at World Congress on Oxidation Catalysis on 6 September 2022 in Cardiff. Talk titled "Digitally-driven routes to new materials and catalysts". |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.scimed.co.uk/seminars-training/9th-world-congress-on-oxidation-catalysis/ |
Description | Postdoctoral researcher (Lin) attended Machine Learning summer school |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Lin (PDRA), attended the Machine Learning summer school held by the EPSRC network Artificial Intelligence for Scientific Discovery at the UoS on 20th - 24th June 2022. The meeting combined chemistry research with advanced computer science techniques to upskill scientists in the field. The event featured a hackathon where participants solved real chemistry problems, and Lin and his subgroup were awarded the Best Project for their well-working program of the defect detection in graphene. |
Year(s) Of Engagement Activity | 2022 |
Description | Postdoctoral researcher (Vasylenko) presented at Materials Research Society Spring Meeting 2022 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Vasylenko (PDRA) presented at the Materials Research Society Spring Meeting 2022 in California. His talk discussed the method developed to learn patterns of similarity between the elemental combinations that afford stability. Further, he demonstrated how the artificial neural network-based model for recognising these patterns in Inorganic Crystal Structure Database can be applied for ranking unexplored combinations of chemical elements at scale. On his return, Vasylenko updated the team about the current positioning of project work in this area versus international activity. |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.mrs.org/meetings-events/spring-meetings-exhibits/past-spring-meetings/2022-mrs-spring-me... |
Description | RSC Interdisciplinary Prize lectures |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Series of lectures given after receiving the RSC Interdisciplinary Prize. https://www.rsc.org/prizes-funding/prizes/find-a-prize/interdisciplinary-prizes/previous-winners/ University of Strathclyde, UK, 26th Oct. 2022 University of Nottingham, UK, 16th Nov. 2022 University of Cambridge, UK, 16th Feb. 2023 |
Year(s) Of Engagement Activity | 2022,2023 |
Description | RSC Solid State Chemistry Group Christmas Meeting 2022 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Poster contribution at RSC Solid State Chemistry Group Christmas Meeting in 2022. Synthesis of Layered Lead-Free Materials by Anion Substitution |
Year(s) Of Engagement Activity | 2022 |
Description | RSC Solid State Chemistry Group Christmas Meeting 2022 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Poster contribution at RSC Solid State Chemistry Group Christmas Meeting in 2022. Title: A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning |
Year(s) Of Engagement Activity | 2022 |
Description | RSC Solid State Chemistry Group Christmas Meeting 2022 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Poster contribution at RSC Solid State Chemistry Group Christmas Meeting in 2022. Title: XPS Investigation of New Oxyfluoride Spinel Li Cathode with Redox-Active Ni and Mn |
Year(s) Of Engagement Activity | 2022 |
Description | RSC Solid State Chemistry Group Christmas Meeting 2022 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Poster contribution at RSC Solid State Chemistry Group Christmas Meeting in 2022. Title: High-performance protonic ceramic fuel cell cathode using protophilic mixed ion and electron conducting material |
Year(s) Of Engagement Activity | 2022 |
Description | RSC Solid State Chemistry Group Christmas Meeting 2022 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Poster contribution at RSC Solid State Chemistry Group Christmas Meeting in 2022. Title: Discovery and property investigation of new intermetallic compounds |
Year(s) Of Engagement Activity | 2022 |
Description | RSC Solid State Chemistry Group Christmas Meeting 2022 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Poster contribution at RSC Solid State Chemistry Group Christmas Meeting in 2022. Title: Single crystal growth and properties of the polar ferromagnet Mn1.05Bi with Kagome layers, huge magnetic anisotropy and slow spin dynamics |
Year(s) Of Engagement Activity | 2022 |
Description | Round table, Harnessing Materials Innovation to Reach Net Zero |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | Together with the New Statesman, convened an online round table on 27th April, 2022 on the topic of Harnessing Materials Innovation to Reach Net Zero. The forum discussion was featured in a New Stateman article. On behalf of the PP project, MJR participated in the symposium with key stakeholders: Prof. Laura Harkness-Brennan (APVC Research & Impact for Science & Engineering, UoL), Bill Esterson MP (Shadow Minister for Business and Industry), Prof. Dame Lynn Gladden (Chair of EPSRC), Dr Jon Hague (VP Science and Technology, Unilever), Metro Mayor Steve Rotheram (Liverpool City Region) and Jon Saltmarsh (Deputy Director of Engineering and Research, Science and Innovation for Climate and Energy Directorate, BEIS). Key messages around the net zero problem requiring physical science input were clearly put to the policy and decision makers, emphasising the disruption in materials requirements and availability that will accompany net zero and the need for the UK to manage risk and seize the arising economic opportunity. |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.newstatesman.com/spotlight/climate-energy-nature/2022/05/how-materials-innovation-can-he... |
Description | SG poster RSC Solid State Chemistry Group Christmas Meeting 2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Sam Goodwin RSC SSCG Christmas Meeting 2023 at University of Edinburgh poster presentation: Li2ZrBr6: A halide-based lithium ion conductor exhibiting a Van der Waals type structure |
Year(s) Of Engagement Activity | 2023 |
Description | Superionic lithium transport via multiple coordination environments defined by two-anion packing |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | 60 pieces of coverage with an estimated reach of 310 million. Main ones highlighted below: Talk Radio Discussion about the discovery of new material for sustainable batteries by UofL researchers MediaView (tveyes.com) Independent Newly discovered material could transform batteries, scientists say https://www.independent.co.uk/tech/battery-li-on-material-ion-b2496957.html Business Desk University researchers discover new material that could transform sustainable batteries https://www.thebusinessdesk.com/northwest/news/2128833-university-researchers-disover-new-material-that-could-transform-sustainable-batteries IEEE Spectrum (provided responses to) New Solid Electrolyte Matches Liquid Performance https://spectrum.ieee.org/battery-electrolyte The Engineer Liverpool team uses AI for battery breakthrough https://www.theengineer.co.uk/content/news/liverpool-team-uses-ai-for-battery-breakthrough/?utm_medium=Social&utm_source=Twitter#Echobox=1708342702-1 MSN News https://www.msn.com/en-us/news/technology/newly-discovered-material-could-transform-batteries-scientists-say/ar-BB1il1Co?ocid=Peregrine Yahoo News https://ca.finance.yahoo.com/news/newly-discovered-material-could-transform-190042856.html SciTechDaily (posted yesterday) New Li-Ion Conductor Discovered - The Novel Material Could Supercharge Electric Vehicle Batteries https://scitechdaily.com/new-li-ion-conductor-discovered-the-novel-material-could-supercharge-electric-vehicle-batteries/ Batteries News https://batteriesnews.com/discovery-of-new-li-ion-conductor-unlocks-new-direction-for-sustainable-batteries/ BioEngineer https://bioengineer.org/discovery-of-new-li-ion-conductor-unlocks-new-direction-for-sustainable-batteries/ Interesting Engineering https://interestingengineering.com/energy/new-solid-material-rapid-liion-conductivity Innovation News Network (provided responses to) https://www.innovationnewsnetwork.com/could-new-lithium-ion-conductor-pave-way-for-sustainable-batteries/43859/ What's New in Electronics https://www.electronicsonline.net.au/content/power/news/li-ion-conductor-discovery-unlocks-new-direction-for-sustainable-batteries-876561267?utm_source=rss Advanced Science News (posted today) Sustainable batteries get a boost from new lithium-ion conductor https://www.advancedsciencenews.com/sustainable-batteries-get-a-boost-from-new-lithium-ion-conductor/?utm_source=twitter&utm_medium=organic&HootpostID=da7c1e9e-2cac-4392-a67f-7d6b84ec0b9f?ofile=advscinews Scienmag https://scienmag.com/discovery-of-new-li-ion-conductor-unlocks-new-direction-for-sustainable-batteries/ The Science Times https://www.sciencetimes.com/articles/48750/20240216/solid-electrolyte-material-addresses-ion-transport-issue-liquid-variety-holds.htm Science Daily https://www.sciencedaily.com/releases/2024/02/240215142138.htm Super Computing Online https://www.supercomputingonline.com/latest/62759-unlocking-a-sustainable-future-for-batteries-is-now-possible-through-a-groundbreaking-discovery AZoMaterials Li-Ion Conductors: A New Pathway to Sustainable Batteries (azom.com) AZoCleantech https://www.azocleantech.com/aboutus.aspx TechnoSpace2 https://ts2.com.pl/en/university-of-liverpool-achieves-breakthrough-in-solid-state-battery-technology/ Nanotechnology Now https://www.nanotech-now.com/news.cgi?story_id=57449 Environment News Network https://www.enn.com/articles/74132-li-ion-conductor-discovery-unlocks-new-direction-for-sustainable-batteries Gearrice https://www.gearrice.com/update/scientists-discover-new-solid-material-that-improves-and-increases-the-capacity-of-batteries/ ExBulletin https://exbulletin.com/tech/2567807/ Mercom India https://www.mercomindia.com/researchers-discover-new-fast-material Dunya Newly discovered material could transform batteries, scientists say - Technology |
Year(s) Of Engagement Activity | 2024 |
Description | Talk at 18th Aarhus Winter Meeting on Trends in Modern Chemistry |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Gave talk titled "Discovery synthesis of inorganic functional materials in the digital age" in Langelandsgade, Denmark at Trends in Modern Chemistry conference. |
Year(s) Of Engagement Activity | 2023 |
URL | https://chem.au.dk/en/the-department/news-and-events/single/artikel/18th-aarhus-winter-meeting |
Description | Workshop hosted on Machine Learning Applications for Chemical Materials Development and Discovery |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | COI, Matthew Dyer co-hosted the "Machine Learning Applications for Chemical Materials Development and Discovery" workshop on 26 January 2022, held in person at UoL. On the day, 57 delegates from academic institutions across the UK attended a series of research talks given by a mixture of speakers both external to UoL and internal. The meeting ended with a 2-hour panel discussion attended by the speakers and hosts to discuss the UK current position on machine learning for materials discovery and formulate a potential road map for the future. |
Year(s) Of Engagement Activity | 2022 |