Multi-objective Optimization for Materials Discovery via Adaptive Design

TLDR

Guiding experiments to find materials with targeted properties is crucial, especially when multiple competing properties are involved. The study seeks new compounds that improve existing Pareto‑front data points for two properties with as few experiments or calculations as possible. Optimal learning methods were applied to three datasets—over 100 shape‑memory alloys, 223 M2AX phases from DFT, and 704 piezoelectric compounds—to assess suitability and performance. Maximin and Centroid design strategies based on value‑of‑information criteria outperformed random, pure exploitation, and pure exploration, with Maximin consistently superior across all datasets even when surrogate models were inaccurate.

Abstract

Guiding experiments to find materials with targeted properties is a crucial aspect of materials discovery and design, and typically multiple properties, which often compete, are involved. In the case of two properties, new compounds are sought that will provide improvement to existing data points lying on the Pareto front (PF) in as few experiments or calculations as possible. Here we address this problem by using the concept and methods of optimal learning to determine their suitability and performance on three materials data sets; an experimental data set of over 100 shape memory alloys, a data set of 223 M2AX phases obtained from density functional theory calculations, and a computational data set of 704 piezoelectric compounds. We show that the Maximin and Centroid design strategies, based on value of information criteria, are more efficient in determining points on the PF from the data than random selection, pure exploitation of the surrogate model prediction or pure exploration by maximum uncertainty from the learning model. Although the datasets varied in size and source, the Maximin algorithm showed superior performance across all the data sets, particularly when the accuracy of the machine learning model fits were not high, emphasizing that the design appears to be quite forgiving of relatively poor surrogate models.