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

TL;DR

This paper presents an integrated approach combining multiscale simulations and machine learning to predict molecular material properties across different spatial scales, addressing complex structure-property relationships.

Contribution

It introduces a novel workflow that leverages multiscale simulation data with machine learning for property prediction of molecular aggregates, bridging nanoscale to macroscale phenomena.

Findings

Effective prediction of molecular properties across scales

Demonstrated integration of multiscale simulations with ML

Potential for accelerated materials discovery

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.