Exploration of new chemical materials using black-box optimization with the D-wave quantum annealer

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Frontiers in Computer Science Pub Date : 2023-12-12 DOI:10.3389/fcomp.2023.1286226
Mikiya Doi, Yoshihiro Nakao, Takuro Tanaka, Masami Sako, Masayuki Ohzeki
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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.
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利用 D 波量子退火器的黑盒优化技术探索新型化学材料
在材料信息学中,由于化学空间浩瀚无边,要搜索具有所需特性的化学材料是一项挑战。此外,由于评估特性的成本较高,因此必须利用少量线索进行搜索。在实践中,还需要提出易于合成的成分。在现实世界中,例如在化学材料的探索中,经常会遇到以黑箱目标函数为目标的问题,在这种情况下,以明确的形式形式化目标函数具有挑战性,而且评估成本很高。最近的研究提出了一种贝叶斯优化方法,将二次无约束二元优化(QUBO)问题表述为具有离散变量的黑箱目标函数的替代模型。关于该方法,已有研究使用 D-Wave 量子退火器来优化获取函数,该方法基于代理模型并确定黑箱目标函数的下一个探索点。在本文中,我们以实际化学材料勘探为背景,探讨优化包含离散变量的黑盒目标函数。在这一优化问题中,我们证明了通过从有方差的概率分布中采样获取获取函数参数的结果,与无方差的情况相比,可以更广泛地探索解空间。因此,我们发现了具有所需特性的成分中的取代基组合,而只有设置适当的方差才能发现这些组合。
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来源期刊
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
期刊最新文献
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