计算凝聚态科学对解决水新兴污染物污染的贡献:全面审查。

IF 2.3 4区 物理与天体物理 Q3 PHYSICS, CONDENSED MATTER
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
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引用次数: 0

摘要

水资源中新兴污染物的研究因其对人类健康和环境的潜在风险而受到广泛关注。本文回顾了计算方法的贡献,重点是机器学习(ML)和分子动力学(MD)模拟的应用,以理解和优化ec在碳基纳米材料上吸附的实验应用。凝聚态物理通过在原子和分子水平上研究材料的基本特性,在本研究中起着至关重要的作用,使设计和工程优化材料的污染物去除成为可能。我们对AMBER、CHARMM、OPLS、GROMOS和COMPASS等各种力场(FFs)进行了全面的讨论,重点介绍了它们的特点、优势以及在分子相互作用建模中的具体应用。本文还深入探讨了ReaxFF等反应电位的发展和应用,这些电位促进了化学反应的大规模原子模拟。此外,我们还探讨了ML模型(包括sGDML和SchNet)如何以更低的计算成本提供高水平的量子描述,从而显著增强经典模型的潜力和精细化。ML与MD模拟的集成允许FFs的精确参数化,提供对吸附机制的详细见解。通过对各种ML模型的定性分析,我们确定了影响吸附能力的关键物理和化学描述符。尽管取得了这些进展,但所研究的内皮细胞多样性有限以及需要广泛的实验验证等挑战仍然存在。这篇综述强调了跨学科合作的重要性,特别是凝聚态物理学的贡献,在开发创新材料和应对ec带来的环境挑战的策略方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational condensed matter science contributions to addressing water emerging contaminant pollution: a comprehensive review.

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.

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来源期刊
Journal of Physics: Condensed Matter
Journal of Physics: Condensed Matter 物理-物理:凝聚态物理
CiteScore
5.30
自引率
7.40%
发文量
1288
审稿时长
2.1 months
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.
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