用深度学习实现原子级晶体结构全自动分析，准确率远超传统方法。
CrystalX: High-accuracy Crystal Structure Analysis Using Deep Learning
- 基于深度学习构建全自动晶体结构解析模型
- 在5万+真实衍射数据上表现优异，超越现有自动化基准
- 可识别同行评审中的隐藏错误，适合材料发现与自驱动实验室
晶体材料的原子结构分析是化学与材料科学的核心任务，传统方法依赖深厚的晶体学知识和复杂软件操作，难以应对日常高通量需求。本文首次提出CrystalX，利用深度学习实现全原子级别的全自动结构分析。我们使用超过5万条来自真实实验的X射线衍射数据进行验证，采用严格的按发表时间划分训练与测试集的时序验证方案。结果显示，CrystalX显著优于现有自动化基线，能精准解析复杂几何特征。令人惊讶的是，即使通过了CheckCIF A/B级严格检查，部分同行评审论文仍存在专家解读错误，而CrystalX能有效识别并修正这些错误。该模型已成功集成至我们的日常研究流程，实现新化合物结构分析的完全自动化、无人干预。CrystalX标志着自驱动实验室中常规结构分析新时代的开启。
原文摘要 · Abstract (English)
Atomic structure analysis of crystalline materials is a paramount endeavor in both chemical and material sciences. This sophisticated technique necessitates not only a solid foundation in crystallography but also a profound comprehension of the intricacies of the accompanying software, posing a significant challenge in meeting the rigorous daily demands. For the first time, we confront this challenge head-on by harnessing the power of deep learning for fully automated routine structure analysis at the full-atom level. To validate the performance of the model, named CrystalX, we employed a dataset comprising over 50,000 X-ray diffraction measurements derived from authentic experiments. Under a strict temporal validation scheme that separates training and test data by publication time, CrystalX substantially outperformed the automated baseline and adept at deciphering intricate geometric patterns. Remarkably, CrystalX revealed that even peer-reviewed publications harbor expert interpretation errors that can evade stringent CheckCIF A/B-level alerts, yet CrystalX adeptly rectifies them. It has already been successfully applied in our day-to-day pipeline, enabling fully automated, human-free structure analysis for newly discovered compounds. Overall, CrystalX marks the beginning of a new era in automating routine structural analysis within self-driving laboratories.
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