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.
@article{arxiv.2007.14832,
title = {Predicting the properties of molecular materials: multiscale simulation workflows meet machine learning},
author = {Fabio Le Piane and Matteo Baldoni and Francesco Mercuri},
journal= {arXiv preprint arXiv:2007.14832},
year = {2021}
}