Application of machine learning in molecular materials discovery
Lead Research Organisation:
Imperial College London
Department Name: Chemistry
Abstract
This project will investigate the use of machine learning, in particular neural networks, for the discovery of new molecular materials. This will focus on porous molecular materials, investigating their formation reactions and properties for separation applications. It will also investigate other organic molecular materials, such as organic molecules for water splitting and hole transport materials. A significant portion of the early work will require building up training sets of data and the use of an evolutionary algorithm in this area.
Organisations
People |
ORCID iD |
| Kim Elizabeth Jelfs (Primary Supervisor) | |
| Lukas Turcani (Student) |
Publications
Berardo E
(2018)
An evolutionary algorithm for the discovery of porous organic cages.
in Chemical science
Miklitz M
(2020)
Computational discovery of molecular C60 encapsulants with an evolutionary algorithm.
in Communications chemistry
Berardo E
(2018)
Computationally-inspired discovery of an unsymmetrical porous organic cage.
in Nanoscale
Wilbraham L
(2018)
High-Throughput Screening Approach for the Optoelectronic Properties of Conjugated Polymers.
in Journal of chemical information and modeling
Tan R
(2020)
Hydrophilic microporous membranes for selective ion separation and flow-battery energy storage.
in Nature materials
Turcani L
(2018)
Machine Learning for Organic Cage Property Prediction
in Chemistry of Materials
Sprick R
(2018)
Maximising the hydrogen evolution activity in organic photocatalysts by co-polymerisation
in Journal of Materials Chemistry A
Turcani L
(2018)
stk: A python toolkit for supramolecular assembly.
in Journal of computational chemistry
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509486/1 | 01/10/2016 | 31/03/2022 | |||
| 1805162 | Studentship | EP/N509486/1 | 01/10/2016 | 29/02/2020 | Lukas Turcani |
| Description | Developed new software that autmatically designs molecules. |
| Exploitation Route | Other people can design new molecules and extend the software to other types of molecules. |
| Sectors | Chemicals,Healthcare,Pharmaceuticals and Medical Biotechnology |
| Title | stk |
| Description | A Python library for the automated design of molcules. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | Used in research for materials discovery. |
| URL | https://github.com/lukasturcani/stk |