Deep Learning with Limited Data for Battery Materials Design

新材料的发现和设计对于推进最先进的电池至关重要，而电池又是推进可再生能源和电动汽车等一系列碳减排技术所必需的。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变得触手可及。然而，QM计算往往限于相对较小的材料集，因为它们的计算成本太高，无法进行大规模筛选，这就是计算新电池材料所需的性能的情况。机器学习中的新方法(ML)已经成为QM计算的一个强大的补充工具-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，到目前为止，这些ML模型被限制在QM属性的相对较大的数据集(数万个或更多实例)可用于训练ML的情况下，从而限制了它们的实用性。在这个项目中，我们将结合我们两个小组(ML在材料设计和电池材料的计算建模方面的专业知识)的专业知识，通过使用迁移学习(TL)的方法来解决这一重要问题。在TL中，先前的模型在大数据集上训练，但在一个明显不同的问题上进行训练，作为学习与电池问题直接相关的新的、较小的数据集的基础。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材料设计中，特别是电池材料。

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.