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arXiv · 2007.14832

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

Fabio Le Piane·Matteo Baldoni·Francesco Mercuri
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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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Fabio Le Piane, Matteo Baldoni, Francesco Mercuri. 2020-07-29. Predicting the properties of molecular materials: multiscale simulation workflows meet machine learning. https://arxiv.org/abs/2007.14832

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