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

TL;DR

ContinuouSP is a novel generative model for crystal structure prediction that effectively incorporates invariance and continuity, improving the ability to generate realistic crystal structures considering symmetry and periodicity.

Contribution

We introduce ContinuouSP, a new energy-based generative model that explicitly handles invariance and continuity in crystal structures, advancing CSP methods.

Findings

Effective handling of symmetry and periodicity in crystals

Preliminary evaluation shows promising results for CSP tasks

Model demonstrates the importance of invariance and continuity in generative CSP

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.