Accelerated Design of New Sustainable Battery Materials with Artificial Intelligence Methods

提供可持续的低碳能源是我们这个时代最紧迫的挑战之一，并提出了根本性的、令人兴奋的科学问题。材料性能是绿色能源技术发展的核心，计算方法现在在模拟能源材料的特性方面起着至关重要的作用。然而，对材料内部的原子过程和控制能量存储设备（如锂离子电池）性能的界面的完全理解仍然不完整。新兴的人工智能（AI）和机器学习技术是强大的工具，为研究从单个原子到数十纳米的长度尺度上的新电池材料提供了创新能力，有希望的量子力学精度和预测能力，同时比传统方法快许多个数量级。该项目的愿景是创新地使用尖端的机器学习模拟技术来探测电池材料的原子级操作，从而实现以前缺失的微观理解，并加速设计具有增强性能的新型可持续材料。随着锂离子电池在便携式电子革命中的成功，我们将解决电动汽车应用的目标，即提高电池电极和固体电解质的能量密度和充电率，特别关注它们的宏观特性如何与微观结构联系起来。该项目将涉及创建精确的拟合数据库和基于机器学习的原子间电位，以模拟新型电池电极和固体电解质的潜在原子行为。在目前的ibm -牛津奖学金项目中，没有类似的协调一致的电池材料人工智能建模项目，将如此不同的专业知识联系起来。

The provision of sustainable low-carbon energy is among the most urgent challenges of our time, and poses fundamental, exciting scientific questions. Materials performance lies at the heart of the development of green energy technologies, and computational methods now play a vital role in modelling the properties of energy materials.However, a full understanding of the atomistic processes within materials and across interfaces that control the performance of energy storage devices such as lithium-ion batteries remains incomplete. Emerging artificial intelligence (AI) and machine learning techniques are powerful tools offering innovative capabilities for studying new battery materials on length scales from individual atoms to tens of nanometres, promising quantum-mechanical accuracy and predictive power, whilst being many orders of magnitude faster than conventional methods.The vision of this project is the innovative use of cutting-edge machine learning simulation techniques to probe the atomic-level operation of battery materials, thereby enabling a previously missing microscopic understanding and an accelerated design of new sustainable materials with enhanced performance. Following the success of the lithium-ion battery in powering the portable electronics revolution, we will address electric vehicle application objectives of increasing the energy density and charge rates of battery electrodes and solid electrolytes, with a particular focus on how their macroscopic properties can be connected to the microscopic structure. The project will involve the creation of accurate fitting databases and machine-learning-based interatomic potentials to model the underlying atomistic behaviour of novel battery electrodes and solid electrolytes.No equivalent concerted AI-modelling project on battery materials that inter-links such different expertise is being undertaken within any current IBM-Oxford Studentship project.