Capturing molecular interactions in graph neural networks: a case study in multi-component phase equilibrium
Shiyi Qin, Shengli Jiang, Jianping Li, Prasanna Balaprakash, Reid C. Van Lehn, Víctor M. Zavala · Digital Discovery · 2022
We propose a graph neural network architecture that captures molecular interactions in an explicit manner by combining atomic-level (local) graph convolution and molecular-level (global) message passing through a molecular interaction network.