Journal Article

Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives

Vergadou, Niki, Constantoudis, Vassilios

2025

Overview

Machine learning (ML) techniques are currently investigated for their potential applicability in a wide range of disciplines and scientific domains as a powerful extension to existing state‐of‐the‐art experimental and computational methods. The diverse scientific areas within the materials science field can largely benefit from the development of data‐driven methods in the present era of advanced ML computational algorithms, efficient, and optimized hardware and large amounts of produced information. In this perspective, basic concepts are introduced and representative advances are showcased from the standpoint of materials characterization and soft matter molecular simulation. Prerequisites and challenges are discussed toward the construction of sound and efficient ML‐aided approaches that can contribute via new auxiliary routes to fundamental understanding and thus facilitate scientific discovery and technological applications. Rigorous frameworks construction toward the development of science‐based machine learning (ML) schemes: invocation of statistical learning and data‐driven methods within the diverse materials science fields, from materials characterization to molecular modeling utilizing domain knowledge to facilitate fundamental understanding and scientific discovery.

• Subject: knowledge‐based machine learning / machine learning / machine learning‐aided multi‐scale modeling / microscopy / molecular simulation