Deep Learning with Limited Data for Battery Materials Design

电池材料设计中数据有限的深度学习

• 批准号: EP/Y000552/1
• 负责人: Keith Butler
• 金额: $ 13.28万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY000552%2F1
• 关键词: Deep; Learning; Limited; Data; Battery

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.

新材料的发现和设计对于推进电池的最新技术至关重要，而这反过来又是推进一系列碳减排技术（如可再生能源和电动汽车）所必需的。新材料的实验发现通常是缓慢和昂贵的，量子力学（QM）计算使计算材料设计触手可及。然而，QM计算通常限于相对较小的材料集合，因为它们的计算成本对于大规模筛选来说太大，这是计算新电池材料所需性能的情况。机器学习（ML）中的新方法已经成为QM计算的强大补充工具-从QM计算的数据中学习规则，并应用廉价，高效的模型来探索大型化学空间。然而，迄今为止，这些ML模型仅限于相对较大的QM属性数据集（数万或更多实例）可用于训练ML的实例，因此限制了它们的实用性。在这个项目中，我们将联合收割机结合我们两个小组的专业知识（材料设计的ML和电池材料的计算建模），通过使用迁移学习（TL）的方法来解决这个重要问题。在TL中，在一个大数据集上训练的先验模型，但在一个明显不同的问题上，被用作在一个新的、与电池问题直接相关的较小数据集上学习的基础。TL在许多其他领域都具有变革性，通过这个项目，我们的目标是将这种潜力带到一般的材料设计中，特别是电池材料。