利用泛型完备等距不变量加速材料性质预测
Accelerating Material Property Prediction using Generically Complete Isometry Invariants
AI总结：
本文提出将泛型完备等距不变量 PDD 作为周期晶体表示，并结合改进自注意力 Transformer 与空间编码，在 Materials Project 和 Jarvis-DFT 上实现与最先进方法相当的预测精度，同时训练和预测速度提升数倍。
摘要：
近年来，使用机器学习进行周期性材料或晶体性质预测日益流行，因为它为经典模拟方法提供了一种计算高效的替代方案。对于任何此类算法而言，关键的第一步是用于表示周期性晶体的方式。虽然分子和蛋白质等类似对象具有有限数量的原子，其表示可以基于有限点云解释来构建，但周期性晶体在尺寸上是无界的，这使得其表示更具挑战性。在本工作中，我们将点向距离分布（Pointwise Distance Distribution，PDD）——一种针对周期点集的连续且泛型完备的等距不变量——适配为学习算法的表示。PDD 在不使用原子类型的情况下，将剑桥结构数据库（Cambridge Structural Database）中全部（超过 66 万个）周期性晶体区分为纯周期点集。我们开发了一个 Transformer 模型，其具有改进的自注意力机制，通过空间编码方法将 PDD 与组成信息相结合。该模型在 Materials Project 和 Jarvis-DFT 数据库的晶体上进行了测试，结果表明其准确性与最先进方法相当，同时在训练和预测时间上快数倍。
英文摘要：
Periodic material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement for classical simulation methods. A crucial first step for any of these algorithms is the representation used for a periodic crystal. While similar objects like molecules and proteins have a finite number of atoms and their representation can be built based upon a finite point cloud interpretation, periodic crystals are unbounded in size, making their representation more challenging. In the present work, we adapt the Pointwise Distance Distribution (PDD), a continuous and generically complete isometry invariant for periodic point sets, as a representation for our learning algorithm. The PDD distinguished all (more than 660 thousand) periodic crystals in the Cambridge Structural Database as purely periodic sets of points without atomic types. We develop a transformer model with a modified self-attention mechanism that combines PDD with compositional information via a spatial encoding method. This model is tested on the crystals of the Materials Project and Jarvis-DFT databases and shown to produce accuracy on par with state-of-the-art methods while being several times faster in both training and prediction time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
