Computational condensed matter science contributions to addressing water emerging contaminant pollution: a...

The study of emerging contaminants (ECs) in water resources has garnered significant attention due to their potential risks to human health and the environment. This review examines the contribution from computational approaches, focusing on the application of machine learning (ML) and molecular dynamics (MD) simulations to understand and optimize experimental applications of ECs adsorption on carbon-based nanomaterials. Condensed matter physics plays a crucial role in this research by investigating the fundamental properties of materials at the atomic and molecular levels, enabling the design and engineering of materials optimized for contaminant removal. We provide a comprehensive discussion of various force fields (FFs) such as AMBER, CHARMM, OPLS, GROMOS, and COMPASS, highlighting their unique features, advantages, and specific applications in modeling molecular interactions. The review also delves into the development and application of reactive potentials like ReaxFF, which facilitate large-scale atomistic simulations of chemical reactions. Additionally, we explore how ML models, including sGDML and SchNet, significantly enhance the potential and refinement of classical models by providing high-level quantum descriptions at reduced computational costs. The integration of ML with MD simulations allows for the accurate parameterization of FFs, offering detailed insights into adsorption mechanisms. Through a qualitative analysis of various ML models applied to the study of ECs on carbon materials, we identify key physical and chemical descriptors influencing adsorption capacities. Despite these advancements, challenges such as the limited diversity of ECs studied and the need for extensive experimental validation persist. This review underscores the importance of interdisciplinary collaboration, particularly the contributions of condensed matter physics, in developing innovative materials and strategies to address the environmental challenges posed by ECs.