José Rafael Bordin, Carolina Ferreira de Matos Jauris, Patrick R B Côrtes, Wanderson S Araújo, Luana S Moreira, Alexsandra Pereira Dos Santos, Mayara Bitencourt Leão, Elizane E Moraes, Maurício J Piotrowski, Mateus H Köhler
{"title":"Computational condensed matter science contributions to addressing water emerging contaminant pollution: a comprehensive review.","authors":"José Rafael Bordin, Carolina Ferreira de Matos Jauris, Patrick R B Côrtes, Wanderson S Araújo, Luana S Moreira, Alexsandra Pereira Dos Santos, Mayara Bitencourt Leão, Elizane E Moraes, Maurício J Piotrowski, Mateus H Köhler","doi":"10.1088/1361-648X/ada65b","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16776,"journal":{"name":"Journal of Physics: Condensed Matter","volume":"37 11","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Physics: Condensed Matter","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/1361-648X/ada65b","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHYSICS, CONDENSED MATTER","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Journal of Physics: Condensed Matter covers the whole of condensed matter physics including soft condensed matter and nanostructures. Papers may report experimental, theoretical and simulation studies. Note that papers must contain fundamental condensed matter science: papers reporting methods of materials preparation or properties of materials without novel condensed matter content will not be accepted.