提出新生成模型ContinuouSP，解决晶体结构预测中的对称性与周期性问题。
ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity
- 基于能量模型构建，显式建模晶体对称性与周期性不变性
- 初步实验验证在晶体结构生成任务中有效提升生成质量
- 适合材料发现、生成模型研究者参考
近年来，基于生成式机器学习模型的晶体结构预测（CSP）已成为重要研究方向。本文研究生成模型中对称性不变性与连续性的关键作用，提出一种名为ContinuouSP的新模型，可有效处理晶体的对称性与周期性特征。通过明确定义不变性与连续性，并基于能量模型构建框架，该模型在初步评估中展现出在晶体结构生成任务中的有效性。
原文摘要 · Abstract (English)
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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