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
Abstract
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.
Topics & Concepts
Computer scienceComponent (thermodynamics)GraphTheoretical computer scienceArtificial neural networkConvolution (computer science)Artificial intelligencePhysicsThermodynamicsComputational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Structure and Dynamics