Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
Abstract
This paper investigates the application of reinforcement learning to automatically optimize lattice parameterization for complex systems, particularly in materials science and molecular modeling. The core claim is to develop a system that dynamically adjusts lattice parameters based on real-time feedback, aiming to improve a desired physical property through optimization. Traditional lattice parameterization methods are static and require manual adjustment, offering a dynamic optimization approach. This research explores the implementation of a reinforcement learning agent to learn optimal parameter settings, leading to improved physical property performance. The paper details the system architecture, the reinforcement learning agent's training process, and the evaluation of its performance on a representative dataset.
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