Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery

Xiaoning Qian; Byung-Jun Yoon; Raymundo Arróyave; Xiaofeng Qian; Edward R Dougherty

doi:10.1016/j.patter.2023.100863

Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery

Patterns (N Y). 2023 Nov 10;4(11):100863. doi: 10.1016/j.patter.2023.100863.

Authors

Xiaoning Qian^{1

2}, Byung-Jun Yoon^{1

2}, Raymundo Arróyave³, Xiaofeng Qian³, Edward R Dougherty¹

Affiliations

¹ Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
² Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA.
³ Department of Materials Science & Engineering, Texas A&M University, College Station, TX 77843, USA.

Abstract

Significant acceleration of the future discovery of novel functional materials requires a fundamental shift from the current materials discovery practice, which is heavily dependent on trial-and-error campaigns and high-throughput screening, to one that builds on knowledge-driven advanced informatics techniques enabled by the latest advances in signal processing and machine learning. In this review, we discuss the major research issues that need to be addressed to expedite this transformation along with the salient challenges involved. We especially focus on Bayesian signal processing and machine learning schemes that are uncertainty aware and physics informed for knowledge-driven learning, robust optimization, and efficient objective-driven experimental design.

Publication types

Review