ED-CSP：基于电子衍射的晶体结构预测
ED-CSP: Crystal Structure Prediction from Electron Diffraction
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中文总结 AI 辅助
该研究提出ED-CSP框架，结合关系集编码器等模块，基于化学成分等预测晶体结构，在ED-CS数据集上训练后，其MR@5指标优于现有模型，具备生成式预测能力，为相关研究建立了基准。
中文摘要 AI 辅助
从稀疏、未索引的电子衍射（ED）观测结果中恢复周期性三维晶体结构是一项具有挑战性的生成式逆问题。现有的基于ED的学习方法主要用于预测晶体学标签、从索引反射重建结构，或从有限结构库中检索候选结构。本文提出ED-CSP，这是一种机器学习框架，可根据化学成分、原子数量以及多个探测器平面的ED斑点集预测晶体结构。ED-CSP结合了关系集编码器、置换不变多视图聚合以及周期流生成器，以联合预测晶格参数和分数原子坐标。为训练该模型，我们构建了ED-CS数据集，包含485万个模拟多视图ED晶体结构，这些结构来自7个材料库且已去重，并过滤掉了与CHILI-100K重叠的部分。在2075个留出的CHILI-100K材料上，仅在CHILI上训练的ED-CSP达到了57.49%的MR@5结构匹配率，优于最先进的晶体结构预测模型PXRDGen（52.92%），该模型以粉末X射线衍射为条件。扩大训练数据可进一步提升性能：从包含100万个结构的预训练模型初始化后，MR@5提升至66.27%。在1024个训练检索库中未出现的成分上，模型仍达到53.52%的MR@5，证明其具有超越精确化学式检索的真正生成能力。将目标ED观测结果替换为相同成分的非同构结构的衍射数据，会使MR@5降低22.09个百分点，证实预测结果依赖于输入的衍射模式而非仅化学成分。ED-CSP和ED-CS为基于稀疏ED观测的生成式晶体结构预测建立了基准，并为未来向实验数据的迁移提供了基础。
英文摘要
Recovering a periodic 3D crystal structure from sparse, unindexed electron diffraction (ED) observations is a challenging generative inverse problem. Existing ED-based learning methods mainly predict crystallographic labels, reconstruct structures from indexed reflections, or retrieve candidates from finite structure libraries. Here, we introduce ED-CSP, a machine learning framework that predicts crystal structures from chemical composition, atom count, and multiple detector-plane ED spot sets. ED-CSP combines a relational set encoder, permutation-invariant multi-view aggregation, and a periodic flow generator to jointly predict lattice parameters and fractional atomic coordinates. To train the model, we construct ED-CS, a dataset of 4.85 million simulated multi-view ED crystal structures, deduplicated across seven materials repositories and filtered to exclude CHILI-100K overlaps. On 2,075 held-out CHILI-100K materials, ED-CSP trained only on CHILI achieves a structural match rate of 57.49% MR@5, outperforming PXRDGen (52.92%), a state-of-the-art crystal structure prediction model conditioned on powder X-ray diffraction. Scaling training data further improves performance: initializing from a one-million-structure precursor raises MR@5 to 66.27%. On 1,024 compositions absent from the training retrieval library, the model still achieves 53.52% MR@5, demonstrating true generative capability beyond exact-formula retrieval. Replacing target ED observations with diffraction from non-isomorphic structures of identical composition decreases MR@5 by 22.09 percentage points, confirming that predictions depend on the input diffraction patterns rather than composition alone. ED-CSP and ED-CS establish a benchmark for generative crystal structure prediction from sparse ED observations and provide a foundation for future transfer to experimental data.
