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Comparison of conceptually different multi-objective Bayesian optimization methods for material design problems
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DOI：10.1016/j.mtcomm.2022.103440.png)
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摘要
En 中文
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.
Keyword:
Multi-objective material design
Bayesian optimization
Machine learning
Benchmark
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期刊
IF:
4.5
论文数:
1.6W
被引数:
3.7W
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