Turning Statistical Physics Models Into Materials Design Engines
Marc Z. Miskin, Gurdaman S. Khaira, Juan J. de Pablo, Heinrich M., Jaeger

TL;DR
This paper introduces a formalism that transforms statistical physics models into automatic optimizers for material design, enabling faster and more effective solutions to complex inverse problems in materials science.
Contribution
The authors develop a novel formalism that extends statistical mechanics to generate optimizers automatically, improving efficiency and applicability over existing black-box methods.
Findings
Generated optimizers outperform standard black-box methods in speed and effectiveness
The approach is straightforward to implement and applicable to equilibrium and non-equilibrium materials
Enables solving complex inverse design problems in materials science
Abstract
Despite the success statistical physics has enjoyed at predicting the properties of materials for given parameters, the inverse problem, identifying which material parameters produce given, desired properties, is only beginning to be addressed. Recently, several methods have emerged across disciplines that draw upon optimization and simulation to create computer programs that tailor material responses to specified behaviors. However, so far the methods developed either involve black-box techniques, in which the optimizer operates without explicit knowledge of the material's configuration space, or they require carefully tuned algorithms with applicability limited to a narrow subclass of materials. Here we introduce a formalism that can generate optimizers automatically by extending statistical mechanics into the realm of design. The strength of this new approach lies in its capability…
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