分子图转换器:超越 ALIGNN,进入远距离相互作用

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-04-23 DOI:10.1039/D4DD00014E
Marco Anselmi, Greg Slabaugh, Rachel Crespo-Otero and Devis Di Tommaso
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引用次数: 0

摘要

图神经网络(GNN)通过直接学习分子和材料的结构信息,为材料特性预测带来了革命性的变化。然而,传统的 GNN 模型仅依赖于局部原子相互作用,例如键长和键角,而忽略了影响某些性质的关键长程静电力。为了解决这个问题,我们引入了分子图转换器(MGT),这是一种新颖的 GNN 架构,它将局部关注机制与键图及其线图上的信息传递相结合,明确捕捉了长程相互作用。在 MatBench 和量子 MOF (QMOF) 数据集上进行的基准测试表明,MGT 对静电相互作用的理解得到了改进,从而显著提高了对剥离能和折射率等性质的预测精度,同时在所有其他性质上保持了最先进的性能。这一突破为在各种应用领域开发高精度、高效率的材料设计工具铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Molecular graph transformer: stepping beyond ALIGNN into long-range interactions†

Graph Neural Networks (GNNs) have revolutionized material property prediction by learning directly from the structural information of molecules and materials. However, conventional GNN models rely solely on local atomic interactions, such as bond lengths and angles, neglecting crucial long-range electrostatic forces that affect certain properties. To address this, we introduce the Molecular Graph Transformer (MGT), a novel GNN architecture that combines local attention mechanisms with message passing on both bond graphs and their line graphs, explicitly capturing long-range interactions. Benchmarking on MatBench and Quantum MOF (QMOF) datasets demonstrates that MGT's improved understanding of electrostatic interactions significantly enhances the prediction accuracy of properties like exfoliation energy and refractive index, while maintaining state-of-the-art performance on all other properties. This breakthrough paves the way for the development of highly accurate and efficient materials design tools across diverse applications.

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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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