Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois, USA; email:
Department of Mechanical Engineering, Boston University, Boston, Massachusetts, USA; email:
Annu Rev Chem Biomol Eng. 2022 Jun 10;13:25-44. doi: 10.1146/annurev-chembioeng-092120-020803. Epub 2022 Mar 2.
This article reviews recent developments in the applications of machine learning, data-driven modeling, transfer learning, and autonomous experimentation for the discovery, design, and optimization of soft and biological materials. The design and engineering of molecules and molecular systems have long been a preoccupation of chemical and biomolecular engineers using a variety of computational and experimental techniques. Increasingly, researchers have looked to emerging and established tools in artificial intelligence and machine learning to integrate with established approaches in chemical science to realize powerful, efficient, and in some cases autonomous platforms for molecular discovery, materials engineering, and process optimization. This review summarizes the basic principles underpinning these techniques and highlights recent successful example applications in autonomous materials discovery, transfer learning, and multi-fidelity active learning.
本文综述了机器学习、数据驱动建模、迁移学习和自主实验在软物质和生物材料的发现、设计和优化中的应用的最新进展。长期以来，化学家和生物分子工程师一直致力于使用各种计算和实验技术来设计和构建分子和分子系统。越来越多的研究人员开始将人工智能和机器学习领域的新兴和成熟工具与化学科学中的成熟方法相结合，以实现强大、高效、并且在某些情况下是自主的分子发现、材料工程和过程优化平台。本文总结了这些技术的基本原理，并重点介绍了自主材料发现、迁移学习和多保真度主动学习方面的最新成功应用案例。
