Work

Acceleration of crystal structure relaxation with deep reinforcement learning

Year: 2025

Type: article

Abstract: We introduce a Deep Reinforcement Learning (DRL) model for crystal structure relaxation and compare different types of neural network architectures and reinforcement learning algorithms for this purpose. Numerical experiments are conducted on Al-Fe structures, with potential energy surfaces generated using EAM potentials. We examine the influence of parameter settings on model performance and benchmark the best-performing models against classical optimization algorithms. Additionally, the model’s capacity to generalize learned interaction patterns from smaller atomic systems to more complex systems is assessed. The results demonstrate the potential of DRL models to enhance the efficiency of structure relaxation compared to classical optimizers.

Source: npj Computational Materials

DOI: 10.1038/s41524-025-01731-1

Authors: Elena Trukhan, Efim Mazhnik, Artem R. Oganov

Institution: Skolkovo Institute of Science and Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,490

Funders: Russian Science Foundation, Skolkovo Institute of Science and Technology