用AI生成1.3亿材料结构，新增7.4万稳定化合物至数据库
AI-Driven Expansion and Application of the Alexandria Database
- 融合生成模型与机器学习势，自动筛选高稳定性材料
- 成功识别99%近热力学稳定化合物，三倍提升前人方法
- 数据开源可用，适合材料设计与力场训练研究者
我们提出一种多阶段计算材料发现流程，实现99%的化合物识别成功率（误差在100 meV/atom内），较之前方法提升三倍。结合Matra-Genoa生成模型、Orb-v2通用机器学习势和ALIGNN图神经网络进行能量预测，生成1.19亿候选结构，并向ALEXANDRIA数据库新增130万经DFT验证的化合物，其中包括7.4万种新稳定材料。扩展后的数据库共包含580万结构、17.5万种位于凸包上的化合物。预测的结构无序率（37-43%）与实验数据库一致，优于近期其他AI生成数据集。分析揭示了空间群分布、配位环境及相稳定网络中的基本规律，包括凸包连通性的亚线性增长。我们发布了完整数据集，包括包含1400万非平衡结构的sAlex25，含力与应力信息，可用于训练通用力场。实验表明，在该数据上微调GRACE模型可提升基准测试准确率。所有数据、模型与工作流均以知识共享许可证开放。
原文摘要 · Abstract (English)
We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we generated 119 million candidate structures and added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74 thousand new stable materials. The expanded ALEXANDRIA database now contains 5.8 million structures with 175 thousand compounds on the convex hull. Predicted structural disorder rates (37-43%) match experimental databases, unlike other recent AI-generated datasets. Analysis reveals fundamental patterns in space group distributions, coordination environments, and phase stability networks, including sub-linear scaling of convex hull connectivity. We release the complete dataset, including sAlex25 with 14 million out-of-equilibrium structures containing forces and stresses for training universal force fields. We demonstrate that fine-tuning a GRACE model on this data improves benchmark accuracy. All data, models, and workflows are freely available under Creative Commons licenses.
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