Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives
Year: 2025
Type: article
Abstract: 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.
Source: ChemPlusChem
Authors: Niki Vergadou, Vassilios Constantoudis
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,030
OpenAlex ID: W4415620292