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Comparison of conceptually different multi-objective Bayesian optimization methods for material design problems

水准点(测量) 计算机科学 协议(科学) 贝叶斯优化 贝叶斯概率 实验设计 数学优化 机器学习 人工智能 数学 医学 统计 替代医学 大地测量学 病理 地理
作者
Kyohei Hanaoka
出处
期刊:Materials today communications [Elsevier BV]
卷期号:31: 103440-103440 被引量:20
标识
DOI:10.1016/j.mtcomm.2022.103440
摘要

For real-world applications, material properties must usually meet multiple requirements, and researchers often spend considerable time designing such materials by trial and error. Multi-objective Bayesian optimization (MOBO) constitutes a promising data-driven solution to accelerate such design problems. As things stand, conceptually different MOBO methods exist for material design problems, such as scalarization- and hypervolume-based methods. However, no standard approach exists to compare how these methods perform and the appropriate choice of MOBO method in each case remains unclear. Herein, a benchmark protocol to compare how conceptually different MOBO methods perform was introduced, based on which the performances of MOBO methods were comprehensively compared using multiple design problems and performance metrics. The benchmark results showed that there was no method that performed best for all combinations of design problems and performance metrics. Moreover, when multiple MOBO methods were compared, the opportunity cost of using each method emerged and it was shown that an inappropriately chosen method can hinder MOBO efficiency. The benchmark results shown here highlight the importance of choosing the right MOBO method and provide guidelines for how this can be done.
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