Support for the UKCP consortium
Lead Research Organisation:
University of Cambridge
Department Name: Materials Science & Metallurgy
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
Publications
Baldock RJN
(2017)
Constant-pressure nested sampling with atomistic dynamics.
in Physical review. E
Hart M
(2017)
Encapsulation and Polymerization of White Phosphorus Inside Single-Wall Carbon Nanotubes
in Angewandte Chemie
Peng F
(2017)
Hydrogen Clathrate Structures in Rare Earth Hydrides at High Pressures: Possible Route to Room-Temperature Superconductivity.
in Physical review letters
Trail J
(2017)
Quantum Monte Carlo study of the energetics of the rutile, anatase, brookite, and columbite TiO 2 polymorphs
in Physical Review B
John ST
(2017)
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials.
in The journal of physical chemistry. B
Trail J
(2017)
Energetics of H 2 clusters from density functional and coupled cluster theories
in Physical Review B
Baldock R
(2017)
Publisher's Note: Constant-pressure nested sampling with atomistic dynamics [Phys. Rev. E 96 , 043311 (2017)]
in Physical Review E
Deng Z
(2017)
Synthesis and Characterization of the Rare-Earth Hybrid Double Perovskites: (CH3NH3)2KGdCl6 and (CH3NH3)2KYCl6.
in The journal of physical chemistry letters
Deringer V
(2017)
Machine learning based interatomic potential for amorphous carbon
in Physical Review B
Hart M
(2017)
Encapsulation and Polymerization of White Phosphorus Inside Single-Wall Carbon Nanotubes.
in Angewandte Chemie (International ed. in English)
| Description | This Consortium grant provides access to the UK's most powerful supercomputer to a group of researchers across many institutions with the collective aim of applying novel computational methods to key scientific questions: these range from creating new machine learning methods to accelerate materials computations, applied dense hydrogen and silicon, to pushing first principles structure prediction methods to complex chemistries, such at those found in potential new battery cathode materials. Given the volume of work that this grant has supported, it would be true to say that it has shifted the state of the art across a wide range of fields. |
| Exploitation Route | CASTEP, a key UKCP code, is sold commercially by Dassault Systemes, but has recently been made available at no cost to the entire global research community. Other codes, such as AIRSS and SHEAP are available under open source licenses. |
| Sectors | Aerospace, Defence and Marine,Chemicals,Construction,Digital/Communication/Information Technologies (including Software),Electronics,Energy,Environment,Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology,Transport |
| URL | https://futurecat.ac.uk/abinitiorandomstructuresearch/ |
| Title | Data for "Stochastic sampling of quadrature grids for the evaluation of vibrational expectation values" |
| Description | Data for "Stochastic sampling of quadrature grids for the evaluation of vibrational expectation values" |
| Type Of Material | Database/Collection of data |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Title | Data set related to the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons" |
| Description | Input/output files for Gaussian calculations, data sets for all plots shown in the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons", C code for the NICS calculations through the dipolar model and python code for the NICS calculations through the tight-binding model described in the manuscript. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3676905 |
| Title | Data set related to the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons" |
| Description | Input/output files for Gaussian calculations, data sets for all plots shown in the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons", C code for the NICS calculations through the dipolar model and python code for the NICS calculations through the tight-binding model described in the manuscript. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3676904 |
| Title | Finite field formalism for bulk electrolyte solutions (data set) |
| Description | All simulations can be run with the LAMMPS code (22 Sept 2017). See also https://github.com/uccasco/FiniteFields for additional source code required to apply the constant D ensemble. Constant E simulations can be performed with in.SPCE.efield. The user will need to replace with the appropriate value of E (along the z direction). Constant D simulations can be performed with in.SPCE.dfield. Likewise, the user will need to replace with the appropriate value of D. Also included are configurations corresponding to different concentrations (these have been obtained from D = 0 simulations). The number of ions pairs and water molecules is given by the filename e.g. 10pair_256wat.data contains 10 ion pairs and 256 water molecules. The name of the configuration file will need to replace the text "" on line 13 of the input files. Note the user may wish to perform shorter simulations for equilibration purposes, especially when changing the value of Dz, or switching to the E ensemble. See included README file. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Title | GAP-20 machine learning force field for phosphorus |
| Description | This dataset contains the force-field parameter files and reference database described in the manuscript "A general-purpose machine-learning force field for bulk and nanostructured phosphorus" (to be published). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4003703 |
| Title | GAP-20 machine learning force field for phosphorus |
| Description | This dataset contains the force-field parameter files and reference database described in the manuscript "A general-purpose machine-learning force field for bulk and nanostructured phosphorus" (to be published). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4003702 |
| Title | Research data supporting "A predictive modelling study of the impact of chemical doping on the strength of a Ag/ZnO interface" |
| Description | The data and lattice structures used to calculate the interfacial adhesion and bond populations are available in this dataset. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Title | Research data supporting "Controlling Ag diffusion in ZnO by donor doping: a first principles study" |
| Description | The data and lattice structures used to calculate the formation energies and diffusion barriers are available in this dataset. The transition state structures used for charge density analysis are also provided. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2017 |
| Provided To Others? | Yes |
| Title | Research data supporting "High-throughput discovery of high-temperature conventional superconductors" |
| Description | Crystal structures of the materials listed in Table. 1 of "High-throughput discovery of high-temperature conventional superconductors", generated using ab initio random structure searching (AIRSS). These are the structures as found to exhibit high-Tc superconductivity after an initial geometry optimization at the listed pressure. They are provided in the CASTEP .cell format and can be easily converted to a number of different formats using the C2x software (https://www.c2x.org.uk/). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/326388 |
| Title | Research data supporting "Investigating Sodium Storage Mechanisms in Tin Anodes: A Combined Pair Distribution Function Analysis, Density Functional Theory, and Solid-State NMR Approach" |
| Description | Raw and processed PDF, XRD, electrochemistry, ssNMR data and CIF files along with corresponding metadata for all measurements published in the paper "Investigating Sodium Storage Mechanisms in Tin Anodes: A Combined Pair Distribution Function Analysis, Density Functional Theory and Solid-State NMR Approach." Specifically, we provide PDF data as .hdf5 or .tif (raw unprocessed) and .csv (integrated and extracted) files, XRD data as .tif (raw unprocessed) and .csv (integrated and extracted) files, electrochemistry data as .csv (plain text) files, and unprocessed NMR data in the IUPAC standard JCAMP-DX format, processed data available as .csv. We refer the reader to the aforementioned paper for further details. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2017 |
| Provided To Others? | Yes |
| Title | Research data supporting "Predicting novel superconducting hydrides using machine learning approaches" |
| Description | |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/303296 |
| Title | Stabilization of AgI's polar surfaces by the aqueous environment, and its implications for ice formation (data set) |
| Description | See also the README file. This dataset contains three sub-directories: (1) D0o0 -- contains input files for performing simulations at D = 0 (2) DCNC -- contains input files for performing simulations at DCNC (2) ECNC -- contains input files for performing simulations at ECNC All simulations can be run with the LAMMPS code (16 Mar 2018). See also https://github.com/uccasco/FiniteFields for additional source code required to apply the E and D fields. Each directory contains the followings files: (a) init.data -- initial structure for pure water in contact with AgI. (b) in.tip4p2005.equil -- input file for performing the initial equilibration of the system at 252K. (c) in.tip4p2005.cool -- input file for performing the cooling ramp simulation between 252K and 242K. (d) in.tip4p2005.constT -- input file for performing a constant T simulation at 242K. The above files perform simulations with an immobile AgI crystal. The DCNC additionally contains a file "in.tip4p2005.constT.mob" which demonstrates the changes needed to perform a simulation with a mobile AgI crystal. (The other equilibration and cooling input files can be similarly adapted.) The ECNC and DCNC directories also contain a file "init.data.electrolyte" which contains an initial structure for NaCl electrolyte in contact with AgI. Please see Table S1 of the article for values of D and E fields used. AgI.table -- tabulated interatomic potential for AgI crystal. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Title | AIRSS |
| Description | Ab initio Random Structure Searching (AIRSS) is a very simple, yet powerful and highly parallel, approach to structure prediction. The concept was introduced in 2006 and its philosophy more extensively discussed in 2011. Random structures - or more precisely, random "sensible" structures - are generated and then relaxed to nearby local energy minima. Particular success has been found using density functional theory (DFT) for the energies, hence the focus on "ab initio" random structure searching. The sensible random structures are constructed so that they have reasonable densities, and atomic separations. Additionally they may embody crystallographic, chemical or prior experimental/computational knowledge. Beyond these explicit constraints the emphasis is on a broad, uniform, sampling of structure space. AIRSS has been used in a number of landmark studies in structure prediction, from the structure of SiH4 under pressure to providing the theoretical structures which are used to understand dense hydrogen (and anticipating the mixed Phase IV), incommensurate phases in aluminium under terapascal pressures, and ionic phases of ammonia. The approach naturally extends to the prediction clusters/molecules, defects in solids, interfaces and surfaces (interfaces with vacuum). The AIRSS package is tightly integrated with the CASTEP first principles total energy code. However, it is relatively straightforward to modify the scripts to use alternative codes to obtain the core functionality, and examples are provided. The AIRSS package is released under the GPL2 licence. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Impact | It appears that researcher are routinely using AIRSS. |
| URL | https://www.mtg.msm.cam.ac.uk/Codes/AIRSS |
| Title | Stochastic Hyperspace Embedding and Projection (SHEAP) |
| Description | Stochastic Hyperspace Embedding And Projection (SHEAP) is a dimensionality reduction method designed for visualising potential energy surfaces. Computational structure prediction can assist the discovery of new materials. One searches for the most stable configurations of a given set of atomic building blocks, which correspond to the deepest regions of an energy landscape-the system's energy as a function of the relative positions of its atoms. To explore these landscapes efficiently, it is important to understand their topologies. However, they exist in spaces with very large numbers of dimensions, making them difficult to visualise. SHEAP uses dimensionality reduction through manifold learning to effectively visualise the distribution of stable structures across a high-dimensional energy landscape. |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | The SHEAP code is being routinely used in our structure searches to map the energy landscape, and help to steer the searches. |
| URL | https://www.mtg.msm.cam.ac.uk/Codes/sheap |