Evolutionary property prediction for molecular materials
Lead Research Organisation:
Imperial College London
Department Name: Chemistry
Abstract
In the simplest of definitions, chemistry concerns the synthesis and the properties of molecules. Supramolecular chemistry is known as "chemistry beyond the molecule", where groups of molecules assemble without forming chemical bonds. Supramolecular systems have exciting applications as sensors, molecular switches, molecular machines (such as molecules that "walk" along a track) and as catalysts that speed up other reactions. We would like to design such systems for new applications by deducing the properties of a supramolecular system from a simple chemical sketch or idea - much as an architect's sketch of a building, for example, can reliably predict its function. However, when we simply draw a molecule, we do not know what properties it will have, nor how it will assemble. Worse, in many cases we cannot be confident that the particular molecule can in fact be synthesised at all since the assembly rules in chemistry are, still, much less well developed than those in architecture. Instead, synthetic chemists use their chemical intuition to guide them as to the best experiments to try. Then, if successful in getting a product, they must characterise the material and its properties. Even in state-of-the-art labs, this is a slow process - a new molecule can take a year to prepare, let alone to characterise. Sometimes even small changes in the reaction can have a large effect on the outcomes, hence 'intuitive' design breaks down, particularly as systems become more complex.
In this proposal, our aim is to provide the same computational 'blueprint' for supramolecular materials in order to allow synthetic research teams to discover new, targeted functions in a much more rapid timeframe. We will develop computer software that will allow us to predict the best molecule for a particular type of device. We aim to use our software for more efficient "sieves" that can separate molecules be size, shape or chemistry, for more efficient molecules for optoelectronic devices such as solar cells and more efficient catalysts for the petrochemical and pharmaceutical industry. The software is based on evolutionary algorithms, these are approaches that are inspired by Darwin's theory of evolution and pit candidate materials against each other as with the "survival of the fittest" in nature. Each generation of candidates is tested with simple calculations that predict their properties as a measure of their fitness. The fittest candidates are most likely to survive to the next generation, but also random mutations of their features will occur and pairs of candidates will parent new offspring with mixtures of their features - just as occurs in nature. These evolutionary approaches are extremely effective ways of exploring very complex problems where there are many variables that influence outcome. The development of this procedure specifically for molecular materials is exciting because it will allow us to direct chemists towards the best synthetic systems and our overarching goal is to show that computational modelling can be responsible for the discovery of new materials with useful new applications, rather than simply rationalising results from synthetic teams. Ultimately we hope this will allow the computational design of new materials to become reliable enough such that it is a routine precursor to synthesis in the laboratory, just as an architect's sketch is the first step to constructing a building.
In this proposal, our aim is to provide the same computational 'blueprint' for supramolecular materials in order to allow synthetic research teams to discover new, targeted functions in a much more rapid timeframe. We will develop computer software that will allow us to predict the best molecule for a particular type of device. We aim to use our software for more efficient "sieves" that can separate molecules be size, shape or chemistry, for more efficient molecules for optoelectronic devices such as solar cells and more efficient catalysts for the petrochemical and pharmaceutical industry. The software is based on evolutionary algorithms, these are approaches that are inspired by Darwin's theory of evolution and pit candidate materials against each other as with the "survival of the fittest" in nature. Each generation of candidates is tested with simple calculations that predict their properties as a measure of their fitness. The fittest candidates are most likely to survive to the next generation, but also random mutations of their features will occur and pairs of candidates will parent new offspring with mixtures of their features - just as occurs in nature. These evolutionary approaches are extremely effective ways of exploring very complex problems where there are many variables that influence outcome. The development of this procedure specifically for molecular materials is exciting because it will allow us to direct chemists towards the best synthetic systems and our overarching goal is to show that computational modelling can be responsible for the discovery of new materials with useful new applications, rather than simply rationalising results from synthetic teams. Ultimately we hope this will allow the computational design of new materials to become reliable enough such that it is a routine precursor to synthesis in the laboratory, just as an architect's sketch is the first step to constructing a building.
Planned Impact
The beneficiaries of this research are academia, petrochemical, pharmaceutical and optoelectronics industry and the general UK population. These benefits are envisaged on a multi-decade timescale. Molecular materials are used, or have the potential to be used, in a wide range of technological applications of importance to the UK economy, including petrochemical and pharmaceutical separations and catalysis, optoelectronics, nanomedicine, sensors and magnetic devices. Our research will also provide insight into the key structural factors affecting the performance of molecular materials. To optimise current devices, for instance improve the energy conversion efficiency and cost of organic solar cells, or to discover new applications, methods to effectively design and thus guide synthetic efforts are needed. There is potential for the predictions to save many person-years and the expense of wasted syntheses of materials that will not have optimal performance. UK-based exploitation of the ensuing patents or devices from our research would ultimately benefit the UK economy. We will provide academic and industrial parties with the opportunity to find optimal molecular materials for a wide range of applications. Through improving efficiency of any of the materials in the multiple listed applications, we have the opportunity to create cheaper devices that are less wasteful of resources in their manufacture. For example, the generation of more efficient organic solar cells can make these devices more economically viable for the wider community, including in developing countries, spreading their use over fossil fuels. This is just one of the potential routes to environmental benefits stemming from the research.
For academics the software we will develop will be freely available and it will be simple to include additional modules for new functionalities to be optimised. Computational modelling is known to add value to the UK economy- for instance between a 3:1 and 9:1 return from R&D investment and an overall contribution of 1% of UK GDP (Goldbeck Consulting Ltd. Report, 2012: http://www.psi-k.org/reports/Economic_impact_of_modelling.pdf). The PDRA hired will be further trained in software development, in molecular materials and in transferable skills, all of which will benefit any future employer. Students at Imperial will be exposed to the approach and the software developed either through lectures, computational lab courses or research projects in the group.
For academics the software we will develop will be freely available and it will be simple to include additional modules for new functionalities to be optimised. Computational modelling is known to add value to the UK economy- for instance between a 3:1 and 9:1 return from R&D investment and an overall contribution of 1% of UK GDP (Goldbeck Consulting Ltd. Report, 2012: http://www.psi-k.org/reports/Economic_impact_of_modelling.pdf). The PDRA hired will be further trained in software development, in molecular materials and in transferable skills, all of which will benefit any future employer. Students at Imperial will be exposed to the approach and the software developed either through lectures, computational lab courses or research projects in the group.
People |
ORCID iD |
Kim Elizabeth Jelfs (Principal Investigator) |
Publications
Berardo E
(2020)
Computational screening for nested organic cage complexes
in Molecular Systems Design & Engineering
Berardo E
(2018)
An evolutionary algorithm for the discovery of porous organic cages.
in Chemical science
Berardo E
(2018)
Computationally-inspired discovery of an unsymmetrical porous organic cage.
in Nanoscale
Greenaway R
(2019)
From Concept to Crystals via Prediction: Multi-Component Organic Cage Pots by Social Self-Sorting
in Angewandte Chemie
Greenaway RL
(2019)
From Concept to Crystals via Prediction: Multi-Component Organic Cage Pots by Social Self-Sorting.
in Angewandte Chemie (International ed. in English)
Jackson E
(2019)
Computational Evaluation of the Diffusion Mechanisms for C8 Aromatics in Porous Organic Cages
in The Journal of Physical Chemistry C
Jelfs K
(2018)
STK: A Python Toolkit for Supramolecular Assembly
Jelfs K
(2018)
pywindow: Automated Structural Analysis of Molecular Pores
Miklitz M
(2017)
Computational Screening of Porous Organic Molecules for Xenon/Krypton Separation
in The Journal of Physical Chemistry C
Miklitz M
(2018)
pywindow: Automated Structural Analysis of Molecular Pores.
in Journal of chemical information and modeling
Description | We have developed open-source software for the discovery of molecular materials, which has led to further funding, new collaborations, publications, prizes and awards and will be used in many further publications in preparation. |
Exploitation Route | Our software will be used in the wider community for materials prediction, once released. Further, when we publish our predictions of promising materials, these can be realised in the laboratory and the resulting materials of use e.g. for improved, more energy efficient separations by membranes, or improved organic electronic materials. By modelling materials in advance of attempts at synthetic realisation, we reduce lost time, money and effort in synthetic attempts to make materials that are not useful, and help guide to promising regions of materials phase space. |
Sectors | Chemicals,Electronics,Energy,Environment |
Description | Computational prediction of large organic polyhedra |
Amount | £110,524 (GBP) |
Funding ID | RPG-2018-239 |
Organisation | The Leverhulme Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 10/2018 |
End | 09/2020 |
Description | Design By Science |
Amount | £651,117 (GBP) |
Funding ID | EP/P005543/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2016 |
End | 08/2019 |
Description | Enhancement Award |
Amount | £99,492 (GBP) |
Funding ID | RGF\EA\180057 - Research Fellows Enhancement Award 2017 |
Organisation | The Royal Society |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 12/2017 |
End | 03/2021 |
Description | Evolutionary screening of coordination cages |
Amount | £248,090 (GBP) |
Funding ID | RGF\EA\181066 |
Organisation | The Royal Society |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 10/2018 |
End | 03/2021 |
Description | H2020 - ERC-2017-STG, Starting Grant |
Amount | € 1,499,390 (EUR) |
Funding ID | 758370 - CoMMaD |
Organisation | European Research Council (ERC) |
Sector | Public |
Country | Belgium |
Start | 04/2018 |
End | 03/2023 |
Description | Dr. Matthew Fuchter |
Organisation | Imperial College London |
Department | Department of Chemistry |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Expertise in the computational simulation of organic electronic materials. Especially focused on structure and property prediction of chiral helicenes. |
Collaborator Contribution | Expertise in helicenes and their use in organic electronics, focused on their synthesis and characterisation in devices. |
Impact | This has resulted in publications, namely papers in 2017 in ACS Nano and Nanoscale. |
Start Year | 2015 |
Description | Prof Jenny Nelson FRS |
Organisation | Imperial College London |
Department | Department of Physics |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | We contribute the software we are developing to do materials discovery, specifically an evolutionary algorithm, and the expertise in its application. We also contribute the chemical understanding of the underlying principles behind the materials that we are conducting predictions on. We also contribute computational hardware that can be used for running simulations. |
Collaborator Contribution | Prof. Jenny Nelson and her group contribute expertise in the multiscale simulation of materials for solar cells and in their characterisation. |
Impact | Myself and my research team established a new research collaboration with Prof. Jenny Nelson in the Department of Physics, Imperial College London. On the basis of the approach and software we had developed in our project, we submitted a successful grant to the EPSRC Design by Science call (detailed in the further funding section). The ongoing collaboration has also meant that I was a co-investigator on the successful ERC Advanced Grant by Prof. Jenny Nelson. Thus far two publications have resulted from the new collaboration (one in ACS Nano and one in Nanoscale, both in 2017). |
Start Year | 2015 |
Title | Supramolecular toolkit |
Description | The software automated the assembly and testing of supramolecular materials |
Type Of Technology | Software |
Year Produced | 2018 |
Open Source License? | Yes |
Impact | Publications resulting from the use of the software, including by other groups. |
URL | https://github.com/JelfsMaterialsGroup/stk |
Description | Royal Society Summer Exhibition - the Hole Story |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Public/other audiences |
Results and Impact | As part of the Royal Society Summer Science Exhibition 2017, which ran for a week (3rd-9th July 2017) at The Royal Society in London, a team led by lead exhibitor Dr Rebecca Greenaway and assisted by Dr. Enrico Berardo (Jelfs group) showcased some of the cutting edge research carried out in their groups via the stand 'Molecular Cages - The Hole Story.' Selected from a competitive application process, the team were selected as one of only 22 exhibitors, and brought to life the research around porous organic cages through a series of hands-on interactives. Around 14,000 people attended during the week of the exhibition, including school children and the general public. |
Year(s) Of Engagement Activity | 2017 |
URL | https://theholestory.wixsite.com/theholestory |