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Bayesian optimization for goal-oriented multi-objective inverse material design.

Kyohei Hanaoka1

  • 1Advanced Technology Research & Development Center, Showa Denko Materials Co., Ltd., 48 Wadai, Tsukuba City, Ibaraki Prefecture 300-4247, Japan.

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This study introduces a goal-oriented multi-objective Bayesian optimization (MO BO) method to accelerate material design. The new MO BO approach significantly reduces experiments needed for complex material property tuning.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Data Science

Background:

  • Material design often requires extensive experimentation to tune multiple properties simultaneously.
  • Existing multi-objective Bayesian optimization (MO BO) methods face challenges in efficiency for time-consuming experimental material design.
  • The complexity of handling multiple objectives hinders the practical application of BO in realistic material design scenarios.

Purpose of the Study:

  • To develop and evaluate a goal-oriented MO BO method for efficient material design.
  • To demonstrate the effectiveness of this approach in accelerating the achievement of predefined material property goals.
  • To clarify the efficiency of MO BO in complex, time-consuming experimental material design.

Main Methods:

  • Introduction of a novel goal-oriented multi-objective Bayesian optimization (MO BO) algorithm.
  • Benchmarking the proposed MO BO method against a baseline approach using simulated material design problems.
  • Conducting virtual inverse design experiments with realistic material design challenges.

Main Results:

  • The goal-oriented MO BO method significantly reduced the number of experiments required to meet predefined goals compared to baseline methods.
  • Virtual experiments showed the method achieved design goals within an average of ten experiments.
  • The approach demonstrated over a 1000-fold acceleration compared to random sampling in the most challenging cases.

Conclusions:

  • Goal-oriented MO BO efficiently accelerates realistic multi-objective material design problems with minimal experimental effort.
  • This targeted approach is crucial for overcoming the complexity of multi-objective optimization in materials discovery.
  • The proposed method paves the way for the practical, real-world application of Bayesian optimization in material design.