Rethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable Targets

• URL: http://arxiv.org/abs/2410.08562v4
• Date: Thu, 29 May 2025 10:11:53 GMT
• Title: Rethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable Targets
• Authors: Akihiro Fujii, Yoshitaka Ushiku, Koji Shimizu, Anh Khoa Augustin Lu, Satoshi Watanabe,
• Abstract summary: We propose simultaneous multi-property optimization using Adaptive Crystal Synthesizer (SMOACS)<n>SMOACS enables multi-property optimization. including exceptional targets such as high-temperature superconductivity, and scales to large crystal systems.<n>In experiments on five target properties and three datasets, SMOACS outperforms generative models and Bayesian optimization methods.
• Score: 7.559885439354167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gradient-based methods offer a simple, efficient strategy for materials design by directly optimizing candidates using gradients from pretrained property predictors. However, their use in crystal structure optimization is hindered by two key challenges: handling non-differentiable constraints, such as charge neutrality and structural fidelity, and susceptibility to poor local minima. We revisit and extend the gradient-based methods to address these issues. We propose Simultaneous Multi-property Optimization using Adaptive Crystal Synthesizer (SMOACS), which integrates oxidation-number masks and template-based initialization to enforce non-differentiable constraints, avoid poor local minima, and flexibly incorporate additional constraints without retraining. SMOACS enables multi-property optimization. including exceptional targets such as high-temperature superconductivity, and scales to large crystal systems, both persistent challenges for generative models, even those enhanced with gradient-based guidance from property predictors. In experiments on five target properties and three datasets, SMOACS outperforms generative models and Bayesian optimization methods, successfully designing 135-atom perovskite structures that satisfy multiple property targets and constraints, a task at which the other methods fail entirely.

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