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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Enhancing Crystal Structure Prediction via Generative Initialization in Genetic Algorithms

Author(s)

Sam Dong, Ajinkya Hire, Jason Gibson, Richard Hennig

On-Site Speaker (Planned)

Sam Dong

Abstract Scope

In the past decade, increased computational power has enabled crystal structure prediction methods such as genetic algorithms (GAs) to discover thermodynamically-viable, novel materials. More recently, generative machine learning (GML) models have emerged as efficient alternatives. However, GML models can be limited in their ability to explore regimes outside of their training manifold, while GAs—though capable of broader exploration—are computationally expensive and often slow to converge due to complex search spaces. A key factor in GA efficiency is population initialization. Current GA strategies rely on randomly generated structures guided by physical heuristics, which can yield poorly optimized candidates and prolong searches. We introduce a generative initialization strategy in which a GML model trained on low-energy structures creates a stronger initial population. This approach improves GA performance at both a local and holistic level, reducing the cost of individual relaxation calculations, while also doubling the rate of discovery.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Other,

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