Self-learning entropic population annealing for interpretable materials design. (12th April 2022)

• Record Type: Journal Article
• Authors: Li, Jiawen; Zhang, Jinzhe; Tamura, Ryo; Tsuda, Koji
• Is Part Of: Digital discovery. Volume 1:Number 3(2022)
• Page Start: 295
• Page End: 302
• Publication Date: 2022-04-12
• Subjects: Chemistry -- Data processing -- Periodicals; Medical sciences -- Data processing -- Periodicals; Machine learning -- Periodicals; 542.85
• DOI: 10.1039/d1dd00043h
• Languages: English
• ISSNs: 2635-098X

Abstract

Abstract : Self-learning entropic population annealing (SLEPA) is an interpretable method for materials design. It achieves efficient optimization without losing statistical consistency. Abstract : In automatic materials design, samples obtained from black-box optimization offer an attractive opportunity for scientists to gain new knowledge. Statistical analyses of the samples are often conducted, e.g., to discover key descriptors. Since most black-box optimization algorithms are biased samplers, post hoc analyses may result in misleading conclusions. To cope with the problem, we propose a new method called self-learning entropic population annealing (SLEPA) that combines entropic sampling and a surrogate machine learning model. Samples of SLEPA come with weights to estimate the joint distribution of the target property and a descriptor of interest correctly. In short peptide design, SLEPA was compared with pure black-box optimization in estimating the residue distributions at multiple thresholds of the target property. While black-box optimization was better at the tail of the target property, SLEPA was better for a wide range of thresholds. Our result shows how to reconcile statistical consistency with efficient optimization in materials discovery.