Data integration for accelerated materials design via preference learning

• Published in: New journal of physics Vol. 22; no. 5; pp. 55001 - 55007
• Main Authors: Sun, Xiaolin, Hou, Zhufeng, Sumita, Masato, Ishihara, Shinsuke, Tamura, Ryo, Tsuda, Koji
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.05.2020
• Subjects: Bayesian analysis; Calibration; Data integration; Data retrieval; Datasets; Design of experiments; Gaussian process; Machine learning; Materials science; Optimization; Organic chemistry; Physics; preference learning; surrogate model
• ISSN: 1367-2630; 1367-2630
• DOI: 10.1088/1367-2630/ab82b9

Machine learning applications in materials science are often hampered by shortage of experimental data. Integration with external datasets from past experiments is a viable way to solve the problem. But complex calibration is often necessary to use the data obtained under different conditions. In this paper, we present a novel calibration-free strategy to enhance the performance of Bayesian optimization with preference learning. The entire learning process is solely based on pairwise comparison of quantities (i.e., higher or lower) in the same dataset, and experimental design can be done without comparing quantities in different datasets. We demonstrate that Bayesian optimization is significantly enhanced via data integration for organic molecules and inorganic solid-state materials. Our method increases the chance that public datasets are reused and may encourage data sharing in various fields of physics.