Reviews in Computational Chemistry最新文献

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Reviews in Computational Chemistry, Volume 32 计算化学评论,第32卷
Reviews in Computational Chemistry Pub Date : 2022-03-25 DOI: 10.1002/9781119625933
G. Crippen
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引用次数: 0
LATTICE‐BOLTZMANN MODELING OF MULTICOMPONENT SYSTEMS 多组分系统的格子BOLTZMANN建模
Reviews in Computational Chemistry Pub Date : 2018-10-19 DOI: 10.1002/9781119518068.CH1
U. Schiller, O. Kuksenok
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引用次数: 0
THE CONSTRUCTION OF AB INITIO‐BASED POTENTIAL ENERGY SURFACES 基于从头算的势能曲面的构造
Reviews in Computational Chemistry Pub Date : 2018-10-19 DOI: 10.1002/9781119518068.CH5
R. Dawes, E. Quintas-Sánchez
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引用次数: 15
INDEX 索引
Reviews in Computational Chemistry Pub Date : 2018-10-19 DOI: 10.1002/9781119518068.index
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引用次数: 0
Reviews in Computational Chemistry, Volume 31 计算化学评论,第31卷
Reviews in Computational Chemistry Pub Date : 2018-10-19 DOI: 10.1002/9781119518068
J. Gauld
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引用次数: 3
THE ROLE OF COMPUTATIONS IN CATALYSIS 计算在催化中的作用
Reviews in Computational Chemistry Pub Date : 2018-10-19 DOI: 10.1002/9781119518068.CH4
H. Metiu, V. Agarwal, H. Kristoffersen
{"title":"THE ROLE OF COMPUTATIONS IN CATALYSIS","authors":"H. Metiu, V. Agarwal, H. Kristoffersen","doi":"10.1002/9781119518068.CH4","DOIUrl":"https://doi.org/10.1002/9781119518068.CH4","url":null,"abstract":"","PeriodicalId":51148,"journal":{"name":"Reviews in Computational Chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/9781119518068.CH4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47847986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MODELING MECHANOCHEMISTRY FROM FIRST PRINCIPLES 从第一性原理建模力学化学
Reviews in Computational Chemistry Pub Date : 2018-10-19 DOI: 10.1002/9781119518068.CH6
H. Kulik
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引用次数: 2
MAPPING ENERGY TRANSPORT NETWORKS IN PROTEINS 绘制蛋白质中的能量传输网络
Reviews in Computational Chemistry Pub Date : 2018-05-09 DOI: 10.1002/9781119518068.CH2
D. Leitner, T. Yamato
{"title":"MAPPING ENERGY TRANSPORT NETWORKS IN PROTEINS","authors":"D. Leitner, T. Yamato","doi":"10.1002/9781119518068.CH2","DOIUrl":"https://doi.org/10.1002/9781119518068.CH2","url":null,"abstract":"The response of proteins to chemical reactions or impulsive excitation that occurs within the molecule has fascinated chemists for decades. In recent years ultrafast X-ray studies have provided ever more detailed information about the evolution of protein structural change following ligand photolysis, and time-resolved IR and Raman techniques, e.g., have provided detailed pictures of the nature and rate of energy transport in peptides and proteins, including recent advances in identifying transport through individual amino acids of several heme proteins. Computational tools to locate energy transport pathways in proteins have also been advancing. Energy transport pathways in proteins have since some time been identified by molecular dynamics (MD) simulations, and more recent efforts have focused on the development of coarse graining approaches, some of which have exploited analogies to thermal transport in other molecular materials. With the identification of pathways in proteins and protein complexes, network analysis has been applied to locate residues that control protein dynamics and possibly allostery, where chemical reactions at one binding site mediate reactions at distance sites of the protein. In this chapter we review approaches for locating computationally energy transport networks in proteins. We present background into energy and thermal transport in condensed phase and macromolecules that underlies the approaches we discuss before turning to a description of the approaches themselves. We also illustrate the application of the computational methods for locating energy transport networks and simulating energy dynamics in proteins with several examples.","PeriodicalId":51148,"journal":{"name":"Reviews in Computational Chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/9781119518068.CH2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44098504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
UNCERTAINTY QUANTIFICATION FOR MOLECULAR DYNAMICS 分子动力学的不确定度量化
Reviews in Computational Chemistry Pub Date : 2018-01-04 DOI: 10.1002/9781119518068.CH3
P. Patrone, A. Dienstfrey
{"title":"UNCERTAINTY QUANTIFICATION FOR MOLECULAR DYNAMICS","authors":"P. Patrone, A. Dienstfrey","doi":"10.1002/9781119518068.CH3","DOIUrl":"https://doi.org/10.1002/9781119518068.CH3","url":null,"abstract":"The goals of this chapter are twofold. First, we wish to introduce molecular dynamics (MD) and uncertainty quantification (UQ) in a common setting in order to demonstrate how the latter can increase confidence in the former. In some cases, this discussion culminates in our providing practical, mathematical tools that can be used to answer the question, \"is this simulation reliable?\" However, many questions remain unanswered. Thus, a second goal of this work is to highlight open problems where progress would aid the larger community.","PeriodicalId":51148,"journal":{"name":"Reviews in Computational Chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/9781119518068.CH3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48645099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Machine Learning in Materials Science 材料科学中的机器学习
Reviews in Computational Chemistry Pub Date : 2016-05-06 DOI: 10.1002/9781119148739.CH4
Tim Mueller, A. Kusne, R. Ramprasad
{"title":"Machine Learning in Materials Science","authors":"Tim Mueller, A. Kusne, R. Ramprasad","doi":"10.1002/9781119148739.CH4","DOIUrl":"https://doi.org/10.1002/9781119148739.CH4","url":null,"abstract":"","PeriodicalId":51148,"journal":{"name":"Reviews in Computational Chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/9781119148739.CH4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50756076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 200
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