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arXiv · 2502.02026

ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity

Yuji Tone·Masatoshi Hanai·Mitsuaki Kawamura·Kenjiro Taura·Toyotaro Suzumura
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Abstract

The discovery of new materials using crystal structure prediction (CSP) based on generative machine learning models has become a significant research topic in recent years. In this paper, we study invariance and continuity in the generative machine learning for CSP. We propose a new model, called ContinuouSP, which effectively handles symmetry and periodicity in crystals. We clearly formulate the invariance and the continuity, and construct a model based on the energy-based model. Our preliminary evaluation demonstrates the effectiveness of this model with the CSP task.

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Yuji Tone, Masatoshi Hanai, Mitsuaki Kawamura, Kenjiro Taura, Toyotaro Suzumura. 2025-02-04. ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity. https://arxiv.org/abs/2502.02026

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