Data-Driven Design and Autonomous Experimentation in Soft and Biological Materials Engineering
Andrew L. Ferguson, Keith A. Brown
Abstract
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.