AI 中文总结
研究利用基础机器学习原子间势和潜在特征增量学习校正DFT形成能至实验精度，通过与其他数据库及实验比较，展示MC3D稳定性，经上述方法降低误差，使结果与实验不确定性相当，还限制了对相对相稳定性的影响。
AI 中文摘要
由高通量密度泛函理论计算整理的晶体结构数据库通常是计算材料发现工作的起点。诸如形成能和凸包上方能量等热力学稳定性数据是指导新型材料搜索的重要量，可用于筛选（亚）稳定结构。本文展示了完全开源、可重现且专注于实验的材料云三维晶体数据库（MC3D）的热力学稳定性。我们将其与另外两个DFT数据库，即开放量子材料数据库（OQMD）和材料项目（MP）以及实验形成焓进行比较。然后证明了如何利用在r$^2$SCAN水平训练的最新基础机器学习原子间势（MLIPs）（具体来说，这里测试了PET-OMATPES）来提高形成能与实验的一致性，相对于GGA，平均绝对误差降低了40%以上，且无需任何额外的DFT计算。我们的结果验证并扩展了将PBEsol几何结构与meta-GGA能量相结合的既定做法到基础MLIPs时代。最后，我们训练经典机器学习模型在增量学习框架中进一步校正形成能，利用基础MLIP的信息丰富的潜在特征。这些模型进一步将平均绝对误差降低到50 meV/原子以下，使其降至与实验不确定性相当的值。值得注意的是，与纯组成特征相比，潜在特征（结合精心调整的正则化）同时降低了预测误差并限制了学习校正对相对相稳定性的影响。
英文摘要
Crystal structure databases curated by high-throughput density functional theory calculations typically serve as the starting point for computational materials discovery efforts. Thermodynamic stability data, such as formation energies and the energy above the convex hull, are important quantities to guide the search for novel materials, enabling filtering for (meta)stable structures. Here, we present the thermodynamic stability of the fully open-source, reproducible, and experimentally focused Materials Cloud three-dimensional crystals database (MC3D). We compare against two other DFT databases, the Open Quantum Materials Database (OQMD) and the Materials Project (MP), as well as against experimental formation enthalpies. We then demonstrate how recent foundational machine learning interatomic potentials (MLIPs) trained at the r$^2$SCAN level (specifically, we test PET-OMATPES here) can be leveraged to improve the agreement of formation energies with experiment, reducing the mean absolute error by more than 40% relative to GGA without requiring any additional DFT calculation. Our results validate and extend the established practice of combining PBEsol geometries with meta-GGA energies to the era of foundational MLIPs. Finally, we train classical machine learning models to further correct the formation energies in a delta-learning framework, where we use the information-rich latent features of the foundational MLIP. These models further reduce the mean absolute error below 50 meV/atom, bringing it down to values comparable with the experimental uncertainty itself. Notably, compared to purely compositional features, the latent features (combined with carefully tuned regularization) simultaneously reduce the prediction error and limit the impact of the learned corrections on the relative phase stability.
