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

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.

@article{tone2025_2502.02026,
  title={ ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity },
  author={ Yuji Tone and Masatoshi Hanai and Mitsuaki Kawamura and Kenjiro Taura and Toyotaro Suzumura },
  journal={arXiv preprint arXiv:2502.02026},
  year={ 2025 }
}