Details of Grant 

EPSRC Reference:	EP/Y000552/1
Title:	Deep Learning with Limited Data for Battery Materials Design
Principal Investigator:	Butler, Dr K T
Other Investigators:
Researcher Co-Investigators:
Project Partners:	Indian Institute of Science IISc
Department:	Chemistry
Organisation:	UCL
Scheme:	Standard Research - NR1
Starts:	01 March 2024	Ends:	28 February 2026	Value (£):	104,199
EPSRC Research Topic Classifications:	Electrochemical Science & Eng.
EPSRC Industrial Sector Classifications:	Energy
Related Grants:
Panel History:	Panel Date Panel Name Outcome 24 May 2023 ECR International Collaboration Grants Panel 3 Announced
Summary on Grant Application Form
The discovery and design of new materials is critical for advancing the state-of-the-art in batteries, which in turn are required for advancing a range of carbon-emission reducing technologies such as renewable energy and electric vehicles. Experimental discovery of new materials is typically slow and costly, quantum mechanics (QM) calculations have brought computational materials design within reach. However, QM calculations are often limited to relatively small sets of materials, as their computational costs are too great for large-scale screening, this is the case for calculating properties required for new battery materials. New methods in machine learning (ML) have emerged as a powerful complementary tool to QM calculations - learning rules from data calculated from QM and applying cheap, efficient models to explore large chemical spaces. However, these ML models have hitherto been restricted to instances where relatively large datasets of QM properties (tens of thousands or more instances) are available for training the ML, thus limiting their utility. In this project we will combine the expertise of our two groups (ML for materials design and computational modelling of battery materials) to tackle this important issue by using the approach of transfer learning (TL). In TL a prior model trained on a large dataset but on an apparently different problem, is used as a foundation to learn on a new, smaller dataset of direct relevance to the battery problem. TL has been transformative in many other fields and with this project we aim to bring this potential to materials design in general and battery materials in particular.
Key Findings
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