Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献_第3页

Dissipative Particle Dynamics Modeling in Polymer Science and Engineering 聚合物科学与工程中的耗散粒子动力学建模

IF 16.8 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2025-04-15 DOI: 10.1002/wcms.70018

Sousa Javan Nikkhah, Matthias Vandichel

{"title":"Dissipative Particle Dynamics Modeling in Polymer Science and Engineering","authors":"Sousa Javan Nikkhah, Matthias Vandichel","doi":"10.1002/wcms.70018","DOIUrl":"https://doi.org/10.1002/wcms.70018","url":null,"abstract":"<p>Polymeric materials are intricate systems with unique properties across different length and time scales, presenting challenges in understanding the hierarchical features that govern their behavior. Advancing innovative polymeric systems requires a deep comprehension of these complexities. Dissipative particle dynamics (DPD), a mesoscale simulation technique, has proven instrumental in elucidating polymer behavior. Unlike molecular dynamics, which tracks individual molecules, DPD employs a coarse-graining approach, to describe molecular systems as particles interacting via soft potentials. Thanks to its computational efficiency, DPD has enabled researchers to numerically study several complex fluid applications in detail. Moreover, with the ever-increasing high-performance computing resources, it has become possible to tackle larger molecular systems beyond the nanoscale, typically micrometer-sized systems. An in-depth analysis of the theoretical foundations of DPD is presented, focusing on its methodology, mathematical formulations, and computational implementation. This review then explores various applications of DPD simulations for polymeric systems, demonstrating DPD's ability to accurately capture phenomena such as polymer self-assembly, polymer behavior in solutions and blends, charged polymers, polymer interfaces, polymer rheology, polymeric membranes, polymerization reactions, and polymeric composites. Overall, this review examines the adoption of DPD as a predictive modeling tool for polymeric materials, focusing on its key features and its integration with methods such as atomistic molecular dynamics to determine the interaction parameters. Building on these advancements, future directions for DPD include its potential applications in other systems like biological membranes, macromolecules, and shape-memory materials.</p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 2","pages":""},"PeriodicalIF":16.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multireference Coupled-Cluster Theory: The Internally Contracted Route 多参考耦合集群理论：内部收缩路线

IF 16.8 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2025-04-15 DOI: 10.1002/wcms.70023

Robert G. Adam, Alexander Waigum, Andreas Köhn

{"title":"Multireference Coupled-Cluster Theory: The Internally Contracted Route","authors":"Robert G. Adam, Alexander Waigum, Andreas Köhn","doi":"10.1002/wcms.70023","DOIUrl":"https://doi.org/10.1002/wcms.70023","url":null,"abstract":"<p>Transferring the success of the coupled-cluster expansion for single-determinant references to multireference cases remains a challenge. The main dilemma is a proper merge of the exponential ansatz, required for extensivity of the correlation energy, with a linear ansatz, required for an unbiased treatment of near-degenerate state interactions. We argue that the state interaction aspect is important and that therefore the Bloch equations are the necessary starting point for all true multireference coupled-cluster theories. Considering the aspect of spin-adaptation and orbital invariance, we arrive at internally contracted expansions, which indeed have a number of appealing formal properties, but also incur a tremendous increase in the complexity of the resulting working equations. The most striking property of internally contracted expansions is probably that a simple transformation of the reference space turns the multistate equations into state-specific equations without introducing further approximations. We discuss the present shortcomings and perspectives of the internally contracted multireference coupled-cluster theory and discuss issues like the completeness of the equations, alternative expansions using normal ordering, and perspectives for large active spaces and large molecules.</p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 2","pages":""},"PeriodicalIF":16.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rational Proteolysis Targeting Chimera Design Driven by Molecular Modeling and Machine Learning 分子建模和机器学习驱动的合理蛋白质分解靶向嵌合体设计

IF 16.8 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2025-03-24 DOI: 10.1002/wcms.70013

Shuoyan Tan, Zhenglu Chen, Ruiqiang Lu, Huanxiang Liu, Xiaojun Yao

{"title":"Rational Proteolysis Targeting Chimera Design Driven by Molecular Modeling and Machine Learning","authors":"Shuoyan Tan, Zhenglu Chen, Ruiqiang Lu, Huanxiang Liu, Xiaojun Yao","doi":"10.1002/wcms.70013","DOIUrl":"https://doi.org/10.1002/wcms.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>Proteolysis targeting chimera (PROTAC) induces specific protein degradation through the ubiquitin–proteasome system and offers significant advantages over small molecule drugs. They are emerging as a promising avenue, particularly in targeting previously “undruggable” targets. Traditional PROTACs have been discovered through large-scale experimental screening. Extensive research efforts have been focused on unraveling the biological and pharmacological functions of PROTACs, with significant strides made toward transitioning from empirical discovery to rational, structure-based design strategies. This review provides an overview of recent representative computer-aided drug design studies focused on PROTACs. We highlight how the utilization of the targeted protein degradation database, molecular modeling techniques, machine learning algorithms, and computational methods contributes to facilitating PROTAC discovery. Furthermore, we conclude the achievements in the PROTAC field and explore challenges and future directions. We aim to offer insights and references for future computational studies and the rational design of PROTACs.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 2","pages":""},"PeriodicalIF":16.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Quantum Monte Carlo for Solids 固体的深度学习量子蒙特卡罗

IF 16.8 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2025-03-24 DOI: 10.1002/wcms.70015

Yubing Qian, Xiang Li, Zhe Li, Weiluo Ren, Ji Chen

{"title":"Deep Learning Quantum Monte Carlo for Solids","authors":"Yubing Qian, Xiang Li, Zhe Li, Weiluo Ren, Ji Chen","doi":"10.1002/wcms.70015","DOIUrl":"https://doi.org/10.1002/wcms.70015","url":null,"abstract":"<div>\u0000 \u0000 <p>Deep learning has deeply changed the paradigms of many research fields. At the heart of chemical and physical sciences is the accurate ab initio calculation of many-body wavefunctions, which has become one of the most notable examples to demonstrate the power of deep learning in science. In particular, the introduction of deep learning into quantum Monte Carlo (QMC) has significantly advanced the frontier of ab initio calculation, offering a universal tool to solve the electronic structure of materials and molecules. Deep learning QMC architectures were initially designed and tested on small molecules, focusing on comparisons with other state-of-the-art ab initio methods. Methodological developments, including extensions to real solids and periodic models, have been rapidly progressing, and reported applications are fast expanding. This review covers the theoretical foundation of deep learning QMC for solids, the neural network wavefunction ansatz, and various other methodological developments. Applications on computing energy, electron density, electric polarization, force, and stress of real solids are also reviewed. The methods have also been extended to other periodic systems and finite temperature calculations. The review highlights the potential and existing challenges of deep learning QMC in materials chemistry and condensed matter physics.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 2","pages":""},"PeriodicalIF":16.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design 催化的未来：应用图神经网络进行智能催化剂设计

IF 16.8 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2025-03-24 DOI: 10.1002/wcms.70010

Zhihao Wang, Wentao Li, Siying Wang, Xiaonan Wang

{"title":"The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design","authors":"Zhihao Wang, Wentao Li, Siying Wang, Xiaonan Wang","doi":"10.1002/wcms.70010","DOIUrl":"https://doi.org/10.1002/wcms.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>With the increasing global demand for energy transition and environmental sustainability, catalysts play a vital role in mitigating global climate change, as they facilitate over 90% of chemical and material conversions. It is important to investigate the complex structures and properties of catalysts for enhanced performance, for which artificial intelligence (AI) methods, especially graph neural networks (GNNs) could be useful. In this article, we explore the cutting-edge applications and future potential of GNNs in intelligent catalyst design. The fundamental theories of GNNs and their practical applications in catalytic material simulation and inverse design are first reviewed. We analyze the critical roles of GNNs in accelerating material screening, performance prediction, reaction pathway analysis, and mechanism modeling. By leveraging graph convolution techniques to accurately represent molecular structures, integrating symmetry constraints to ensure physical consistency, and applying generative models to efficiently explore the design space, these approaches work synergistically to enhance the efficiency and accuracy of catalyst design. Furthermore, we highlight high-quality databases crucial for catalysis research and explore the innovative application of GNNs in thermocatalysis, electrocatalysis, photocatalysis, and biocatalysis. In the end, we highlight key directions for advancing GNNs in catalysis: dynamic frameworks for real-time conditions, hierarchical models linking atomic details to catalyst features, multi-task networks for performance prediction, and interpretability mechanisms to reveal critical reaction pathways. We believe these advancements will significantly broaden the role of GNNs in catalysis science, paving the way for more efficient, accurate, and sustainable catalyst design methodologies.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 2","pages":""},"PeriodicalIF":16.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Traditional Methods to Deep Learning Approaches: Advances in Protein–Protein Docking 从传统方法到深度学习方法：蛋白质-蛋白质对接的进展

IF 16.8 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2025-03-24 DOI: 10.1002/wcms.70016

Linlong Jiang, Ke Zhang, Kai Zhu, Hui Zhang, Chao Shen, Tingjun Hou

{"title":"From Traditional Methods to Deep Learning Approaches: Advances in Protein–Protein Docking","authors":"Linlong Jiang, Ke Zhang, Kai Zhu, Hui Zhang, Chao Shen, Tingjun Hou","doi":"10.1002/wcms.70016","DOIUrl":"https://doi.org/10.1002/wcms.70016","url":null,"abstract":"<div>\u0000 \u0000 <p>Protein–protein interactions play a crucial role in human biological processes, and deciphering their structural information and interaction patterns is essential for drug development. The high costs of experimental structure determination have brought computational protein–protein docking methods into the spotlight. Traditional docking algorithms, which hinge on a sampling-scoring framework, heavily rely on extensive sampling of candidate poses and customized scoring functions based on the geometric and chemical compatibility between proteins. However, these methods face challenges related to sampling efficiency and stability. The advent of deep learning (DL) has ushered in data-driven docking methods that demonstrate significant advantages, particularly boosting the efficiency of protein–protein docking. We systematically review the historical development of protein–protein docking from traditional approaches to DL techniques and provide insights into emerging technologies in this field. Moreover, we summarize the commonly used datasets and evaluation metrics in protein–protein docking. We expect that this review can offer valuable guidance for the development of more efficient protein–protein docking algorithms.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 2","pages":""},"PeriodicalIF":16.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spillover Dynamics in Heterogeneous Catalysis on Singe-Atom Alloys: A Theoretical Perspective 单原子合金非均相催化的溢出动力学：一个理论视角

IF 16.8 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2025-03-24 DOI: 10.1002/wcms.70011

Sutao Lin, Rui Xiong, Jun Chen, Sen Lin

{"title":"Spillover Dynamics in Heterogeneous Catalysis on Singe-Atom Alloys: A Theoretical Perspective","authors":"Sutao Lin, Rui Xiong, Jun Chen, Sen Lin","doi":"10.1002/wcms.70011","DOIUrl":"https://doi.org/10.1002/wcms.70011","url":null,"abstract":"<div>\u0000 \u0000 <p>Recent advances in single-atom alloy (SAA) catalysts provide a unique platform for understanding spillover, due to the well-defined nature of the active site for dissociative chemisorption. In particular, the use of spilled adsorbates following molecular dissociation on the host metal surface facilitates the generation of high-value chemicals in subsequent catalytic reactions. Nevertheless, the factors that control the spillover process remain to be fully elucidated. This perspective discusses recent theoretical advances in the spillover dynamics on SAAs, with a particular focus on the dissociation and spillover processes of H<sub>2</sub> and CH<sub>4</sub>. It provides valuable insights into how various factors, such as energy transfer, nuclear quantum effects, gas-adsorbate interactions, and adsorbate size, impact the diffusion behavior of hydrogen and methyl species on SAA surfaces. The article concludes with a discussion of future prospects. This perspective underscores the significance of spillover dynamics in heterogeneous catalysis, with important implications for improving catalytic performance.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 2","pages":""},"PeriodicalIF":16.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing GPU-Acceleration in the Python-Based Simulations of Chemistry Frameworks 在基于 Python 的化学框架模拟中增强 GPU 加速能力

IF 16.8 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2025-03-23 DOI: 10.1002/wcms.70008

Xiaojie Wu, Qiming Sun, Zhichen Pu, Tianze Zheng, Wenzhi Ma, Wen Yan, Yu Xia, Zhengxiao Wu, Mian Huo, Xiang Li, Weiluo Ren, Sheng Gong, Yumin Zhang, Weihao Gao

{"title":"Enhancing GPU-Acceleration in the Python-Based Simulations of Chemistry Frameworks","authors":"Xiaojie Wu, Qiming Sun, Zhichen Pu, Tianze Zheng, Wenzhi Ma, Wen Yan, Yu Xia, Zhengxiao Wu, Mian Huo, Xiang Li, Weiluo Ren, Sheng Gong, Yumin Zhang, Weihao Gao","doi":"10.1002/wcms.70008","DOIUrl":"https://doi.org/10.1002/wcms.70008","url":null,"abstract":"<div>\u0000 \u0000 <p>We describe our contribution as industrial stakeholders to the existing open-source GPU4PySCF project (https://github.com/pyscf/gpu4pyscf), a GPU-accelerated Python quantum chemistry package. We have integrated GPU acceleration into other PySCF functionalities including Density Functional Theory (DFT), geometry optimization, frequency analysis, solvent models, and the density fitting technique. Through these contributions, GPU4PySCF v1.0 can now be regarded as a fully functional and industrially relevant platform, which we demonstrate in this work through a range of tests. When performing DFT calculations with the density fitting scheme on modern GPU platforms, GPU4PySCF delivers a 30 times speedup over a 32-core CPU node, resulting in approximately 90% cost savings for most DFT tasks. The performance advantages and productivity improvements have been found in multiple industrial applications, such as generating potential energy surfaces, analyzing molecular properties, calculating solvation free energy, identifying chemical reactions in lithium-ion batteries, and accelerating neural-network methods. With the improved design that makes it easy to integrate with the Python and PySCF ecosystem, GPU4PySCF is a natural choice that we can now recommend for many industrial quantum chemistry applications.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 2","pages":""},"PeriodicalIF":16.8,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances and Challenges of SCAN and r2SCAN Density Functionals in Transition-Metal Compounds 过渡金属化合物中SCAN和r2SCAN密度泛函的研究进展与挑战

IF 16.8 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2025-03-23 DOI: 10.1002/wcms.70007

Yubo Zhang, Akilan Ramasamy, Kanun Pokharel, Manish Kothakonda, Bing Xiao, James W. Furness, Jinliang Ning, Ruiqi Zhang, Jianwei Sun

{"title":"Advances and Challenges of SCAN and r2SCAN Density Functionals in Transition-Metal Compounds","authors":"Yubo Zhang, Akilan Ramasamy, Kanun Pokharel, Manish Kothakonda, Bing Xiao, James W. Furness, Jinliang Ning, Ruiqi Zhang, Jianwei Sun","doi":"10.1002/wcms.70007","DOIUrl":"https://doi.org/10.1002/wcms.70007","url":null,"abstract":"<div>\u0000 \u0000 <p>Transition-metal compounds (TMCs) with open-shell <i>d</i>-electrons are characterized by a complex interplay of lattice, charge, orbital, and spin degrees of freedom, giving rise to various fascinating applications. Often exhibiting exotic properties, these compounds are commonly classified as correlated systems due to strong inter-electronic interactions called Hubbard <i>U</i>. This inherent complexity presents significant challenges to Kohn-Sham density functional theory (KS-DFT), the most widely used electronic structure method in condensed matter physics and materials science. While KS-DFT is, in principle, exact for the ground-state total energy, its exchange-correlation energy must be approximated in practice. The mean-field nature of KS implementations, combined with the limitations of current exchange-correlation density functional approximations, has led to the perception that DFT is inadequate for correlated systems, particularly TMCs. Consequently, a common workaround involves augmenting DFT with an on-site Hubbard-like <i>U</i> correction. In recent years, the <i>strongly constrained and appropriately normed</i> (SCAN) density functional, along with its refined variant r<sup>2</sup>SCAN, has achieved remarkable progress in accurately describing the structural, energetic, electronic, magnetic, and vibrational properties of TMCs, challenging the traditional perception of DFT's limitations. This review explores the design principles of SCAN and r<sup>2</sup>SCAN, highlights their key advancements in studying TMCs, explains the mechanisms driving these improvements, and addresses the remaining challenges in this evolving field.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 2","pages":""},"PeriodicalIF":16.8,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Molecular Dynamics Simulations of Electrical Double Layers: From Simplified to Realistic Models 加强电双层分子动力学模拟：从简化到现实模型

IF 16.8 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2025-03-23 DOI: 10.1002/wcms.70009

Liang Zeng, Xiangyu Ji, Jinkai Zhang, Nan Huang, Zhenxiang Wang, Ding Yu, Jiaxing Peng, Guang Feng

{"title":"Enhancing Molecular Dynamics Simulations of Electrical Double Layers: From Simplified to Realistic Models","authors":"Liang Zeng, Xiangyu Ji, Jinkai Zhang, Nan Huang, Zhenxiang Wang, Ding Yu, Jiaxing Peng, Guang Feng","doi":"10.1002/wcms.70009","DOIUrl":"https://doi.org/10.1002/wcms.70009","url":null,"abstract":"<div>\u0000 \u0000 <p>Molecular dynamics (MD) simulations have become a powerful tool for studying double-layer systems, offering atomistic insights into their equilibrium properties and dynamic behaviors. These simulations have significantly advanced the understanding of key electrochemical mechanisms and the design of electrochemical devices. However, challenges remain in aligning simulations with the complexities of realistic applications. In this perspectiv, we highlight critical areas for enhancing the realism of MD simulations, including refining methods for representing electrode polarization, improving electrode and electrolyte models to incorporate structural and compositional complexities, and simulating charging and discharging processes under realistic conditions while considering associated thermal behaviors. We also stress the importance of scaling simulation results to experimental dimensions through multiscale modeling and dimensionless analysis. Overcoming these challenges will allow MD simulations to advance our understanding of electrical double-layer behaviors and drive innovations in the development of future electrochemical technologies.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 2","pages":""},"PeriodicalIF":16.8,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0