Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; email:
Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Annu Rev Chem Biomol Eng. 2022 Jun 10;13:405-429. doi: 10.1146/annurev-chembioeng-092320-120230. Epub 2022 Mar 23.
Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward () the discovery of new materials through large-scale enumerative screening, () the design of materials through identification of rules and principles that govern materials properties, and () the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.
机器学习（ML）已经成为高通量筛选和材料计算发现的重要组成部分。尽管它的作用越来越重要，但要充分实现 ML 的承诺仍然存在挑战。对于实际加速稳健材料的工程设计和开发超越试错或高通量筛选的设计策略尤其如此。具体取决于要预测的数量和可用的实验数据，ML 可以胜过基于物理的模型，用于加速这些模型，或者与它们集成以提高其性能。我们介绍了算法的最新进展及其应用，这些进展开始在以下方面取得突破：（）通过大规模枚举筛选发现新材料，（）通过确定控制材料性能的规则和原则来设计材料，（）通过满足多个目标来设计实际材料。最后，我们探讨了进一步发展的机会，以实现 ML 作为实用计算材料设计的广泛工具。
