融合智能生成与物理优化，高效预测材料晶体结构。
CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction
- 用预训练生成模型结合空间群信息产结构初稿。
- 通过通用机器学习势能场实现能量最小化优化。
- 支持网页端与语言模型接入，适合材料研发人员。
晶体结构预测是材料科学中的基础问题。本文提出CrystalFormer-CSP，一种高效框架，融合数据驱动启发式方法与物理驱动优化方法，针对给定化学组成预测稳定晶体结构。该方法结合预训练生成模型以空间群信息指导结构生成，并利用通用机器学习力场进行能量最小化。可通过强化学习微调进一步提升精度。我们在基准任务上验证了其有效性，并展示了通过网页界面和语言模型集成的应用方式。
原文摘要 · Abstract (English)
Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driven optimization approaches to predict stable crystal structures for given chemical compositions. The approach combines pretrained generative models for space-group-informed structure generation and a universal machine learning force field for energy minimization. Reinforcement fine-tuning can be employed to further boost the accuracy of the framework. We demonstrate the effectiveness of CrystalFormer-CSP on benchmark problems and showcase its usage via web interface and language model integration.
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