Leveraging feature gradient for efficient acquisition function maximization in material composition design

doi:10.1039/D5DD00080G

Leveraging feature gradient for efficient acquisition function maximization in material composition design

2025-07-26

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DOI：10.1039/D5DD00080G

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Bayesian optimization (BO) has been widely employed for alloy composition design; but faces unique challenges in this domain when maximizing the acquisition function (AF); which is a critical step for selecting the best candidate. While various optimization methods exist for maximizing AF; material composition design presents difficulties that include the need to translate compositions into material features; rapid polynomially expanding design spaces as component numbers increase; and compositional constraints (e.g.; sum to 100%). To address this issue; we propose a strategy that leverages numerical feature gradient for efficient AF maximization in material composition design. By establishing a differentiable pipeline from alloy compositions; through material features and model predictions; to AF values; our strategy enables efficient navigation from initial compositional guesses to optimal solutions. This approach reduces the complexity of the inner optimization problem from rapid polynomial (i.e.; in the case of full enumeration) to empirically observed linear scale with respect to the number of components; making it efficient for medium-scaled design spaces (up to 10 components) while showing potential for scaling to larger compositional spaces. Additionally; initiating the process with randomly generated compositions promotes more diverse solutions; as evidenced by a slower decay of compositional state entropy compared to traditional enumeration-based approaches. Furthermore; the flexibility of our method allows for tailoring the optimization process by adjusting key settings; such as the number of initial compositions; the choice of AFs; surrogate models; and the formulas used to calculate material features. We envision this strategy as a scalable and modular methodology for advancing materials design; particularly in the composition design of high-entropy alloys; ceramics; and perovskites; where elemental compositions can be adjusted as continuous variables.

Keywords:

Bayesian optimization

acquisition function maximization

material composition design

differentiable pipeline

high-entropy alloys

Leveraging feature gradient for efficient acquisition function maximization in material composition design

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