Predicting the properties of molecular materials: multiscale simulation workflows meet machine learning

2020/07/29 by Fabio Le Piane, Piane, Fabio Le, Matteo Baldoni +3
Materials Science · Computer Science · Chemical Engineering · #Machine Learning in Materials Science #Computational Drug Discovery Methods #Catalysis and Oxidation Reactions

paper · pdf · doi:10.48550/arxiv.2007.14832

Abstract

Machine Learning tools are nowadays widely applied extensively to the prediction of the properties of molecular materials, using datasets extracted from high-throughput computational models. In several cases of scientific and technological relevance, the properties of molecular materials are related to the link between molecular structure and phenomena occurring across a wide set of spatial scales, from the nanoscale to the macroscale. Here, we describe an approach for predicting the properties of molecular aggregates based on multiscale simulations and machine learning.

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