2022/03/07 by Ikki Yasuda, Yasuda, Ikki, Yusei Kobayashi +12
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Fuel Cells and Related Materials #Machine Learning (cs.LG) #Machine Learning in Materials Science #Protein Structure and Dynamics #Soft Condensed Matter (cond-mat.soft)
paper · pdf · doi:10.48550/arxiv.2203.03103
openalex publication_date 2022/03/07 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are represented by multiple features, such as thermodynamic properties, and are used as the ML input. However, to accurately find the input--output patterns, ML requires a sufficiently sized dataset that depends on the complexity of the ML model. Generating such a large dataset from MD simulations is not ideal because of their high computation cost. In this study, we present a simple supervised ML method to predict the transport properties of materials. To simplify the model, an unsupervised ML method obtains an efficient representation of molecular movements. This method was applied to predict the viscosity of lubricant molecules in confinement with shear flow. Furthermore, simplicity facilitates the interpretation of the model to understand the molecular mechanics of viscosity. We revealed two types of molecular mechanisms that contribute to low viscosity.