论文标题
周期表的统一图神经网络力场
Unified Graph Neural Network Force-field for the Periodic Table
论文作者
论文摘要
基于机器学习（ML）方法的经典力场（FF）显示了大规模材料模拟的巨大潜力。迄今为止，MLFF在很大程度上是针对特定系统设计和安装的，通常无法将其转移到特定训练集之外的化学物质。我们开发了一个统一的原子图神经网络基于基于的FF（Alignn-FF），该FF（Alignn-FF）可以在结构和化学上多样化的材料与周期表中的89个元素组合进行建模。为了训练Alignn-FF型号，我们使用JARVIS-DFT数据集，该数据集包含约75000材料和400万个能量强度的条目，其中307113在培训中使用。我们证明了这种方法在晶体学开放数据库中快速优化原子结构的适用性，并通过使用合金的遗传算法预测准确的晶体结构。
Classical force fields (FF) based on machine learning (ML) methods show great potential for large scale simulations of materials. MLFFs have hitherto largely been designed and fitted for specific systems and are not usually transferable to chemistries beyond the specific training set. We develop a unified atomisitic line graph neural network-based FF (ALIGNN-FF) that can model both structurally and chemically diverse materials with any combination of 89 elements from the periodic table. To train the ALIGNN-FF model, we use the JARVIS-DFT dataset which contains around 75000 materials and 4 million energy-force entries, out of which 307113 are used in the training. We demonstrate the applicability of this method for fast optimization of atomic structures in the crystallography open database and by predicting accurate crystal structures using genetic algorithm for alloys.
