AUTHOR=Doi Mikiya , Nakao Yoshihiro , Tanaka Takuro , Sako Masami , Ohzeki Masayuki TITLE=Exploration of new chemical materials using black-box optimization with the D-wave quantum annealer JOURNAL=Frontiers in Computer Science VOLUME=5 YEAR=2023 URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2023.1286226 DOI=10.3389/fcomp.2023.1286226 ISSN=2624-9898 ABSTRACT=

In materials informatics, searching for chemical materials with desired properties is challenging due to the vastness of the chemical space. Moreover, the high cost of evaluating properties necessitates a search with a few clues. In practice, there is also a demand for proposing compositions that are easily synthesizable. In the real world, such as in the exploration of chemical materials, it is common to encounter problems targeting black-box objective functions where formalizing the objective function in explicit form is challenging, and the evaluation cost is high. In recent research, a Bayesian optimization method has been proposed to formulate the quadratic unconstrained binary optimization (QUBO) problem as a surrogate model for black-box objective functions with discrete variables. Regarding this method, studies have been conducted using the D-Wave quantum annealer to optimize the acquisition function, which is based on the surrogate model and determines the next exploration point for the black-box objective function. In this paper, we address optimizing a black-box objective function containing discrete variables in the context of actual chemical material exploration. In this optimization problem, we demonstrate results obtaining parameters of the acquisition function by sampling from a probability distribution with variance can explore the solution space more extensively than in the case of no variance. As a result, we found combinations of substituents in compositions with the desired properties, which could only be discovered when we set an appropriate variance.