论文标题
通用图的深度学习周期表的原子间潜力
A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
论文作者
论文摘要
描述原子势能表面的原子间电位（IAP）是原子模拟的基本输入。但是，现有的IAP要么适合狭窄的化学物质，要么对于一般应用不准确。在这里，我们报告了基于具有三体相互作用（M3GNET）的图神经网络的材料的通用IAP。在过去的10年中，M3GNET IAP在材料项目执行的大规模结构放松数据库上进行了训练，并在结构放松，动态模拟和对各种化学空间的材料的财产预测中有广泛的应用。从3100万个假设晶体结构的筛选中，发现了约180万种材料，以基于M3GNET能量的现有材料项目晶体具有稳定性。在船体上方最低能量的前2000个材料中，使用DFT计算证实了1578材料是稳定的。这些结果证明了机器学习加速的途径，可以发现具有特性特性的合成材料。
Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. Here, we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past 10 years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. About 1.8 million materials were identified from a screening of 31 million hypothetical crystal structures to be potentially stable against existing Materials Project crystals based on M3GNet energies. Of the top 2000 materials with the lowest energies above hull, 1578 were verified to be stable using DFT calculations. These results demonstrate a machine learning-accelerated pathway to the discovery of synthesizable materials with exceptional properties.
