Rethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable Targets
This addresses a domain-specific problem for materials science by enabling multi-property optimization in crystal design, though it is incremental as it extends existing gradient-based methods.
The paper tackled the problem of optimizing crystal structures for multiple properties using gradient-based methods, which are hindered by non-differentiable constraints and local minima, by proposing SMOACS to enforce constraints and avoid minima, resulting in successful design of 135-atom perovskite structures that outperform generative models and Bayesian optimization on five target properties.
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.