Atomistic Line Graph Neural Network for improved materials property predictions
Atomistic Line Graph Neural Network for improved materials property predictions
复制标题
DOI：
10.1038/s41524-021-00650-1
复制
发表时间：
2021-11-15
影响因子：
9.7
通讯作者：
DeCost, Brian
中科院分区：
文献类型：
--
作者：
Choudhary, Kamal;DeCost, Brian
与基于描述符的机器学习模型相比，图神经网络(GNN)在原子化材料表示和建模方面具有显著的性能改进。虽然大多数现有的GNN原子预测模型都是基于原子距离信息，但它们并没有明确地包含键角，这对区分许多原子结构至关重要。此外，众所周知，许多材料的性质对键角的微小变化很敏感。我们提出了一种原子化线图神经网络(ALIGNN)，这是一种GNN结构，它在原子间键图及其对应键角的线图上执行消息传递。我们证明了角度信息可以被显式且有效地包括在内，从而提高了多原子预测任务的性能。我们使用ALIGNN模型来预测Jarvis-DFT、材料项目和QM9数据库中提供的52个固态和分子性质。在原子性预测任务上，ALIGNN比以往报道的GNN模型的准确率高达85%，模型训练速度更好或相当。
Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for atomistic predictions are based on atomic distance information, they do not explicitly incorporate bond angles, which are critical for distinguishing many atomic structures. Furthermore, many material properties are known to be sensitive to slight changes in bond angles. We present an Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond angles. We demonstrate that angle information can be explicitly and efficiently included, leading to improved performance on multiple atomistic prediction tasks. We ALIGNN models for predicting 52 solid-state and molecular properties available in the JARVIS-DFT, Materials project, and QM9 databases. ALIGNN can outperform some previously reported GNN models on atomistic prediction tasks by up to 85% in accuracy with better or comparable model training speed.