Pub Date : 2018-10-19DOI: 10.1002/9781119518068.CH1
U. Schiller, O. Kuksenok
{"title":"LATTICE‐BOLTZMANN MODELING OF MULTICOMPONENT SYSTEMS","authors":"U. Schiller, O. Kuksenok","doi":"10.1002/9781119518068.CH1","DOIUrl":"https://doi.org/10.1002/9781119518068.CH1","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.CH1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43158940","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}
Pub Date : 2018-10-19DOI: 10.1002/9781119518068.CH5
R. Dawes, E. Quintas-Sánchez
{"title":"THE CONSTRUCTION OF AB INITIO‐BASED POTENTIAL ENERGY SURFACES","authors":"R. Dawes, E. Quintas-Sánchez","doi":"10.1002/9781119518068.CH5","DOIUrl":"https://doi.org/10.1002/9781119518068.CH5","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.CH5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41636300","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}
Pub Date : 2018-10-19DOI: 10.1002/9781119518068.index
{"title":"INDEX","authors":"","doi":"10.1002/9781119518068.index","DOIUrl":"https://doi.org/10.1002/9781119518068.index","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.index","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46649045","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}
Pub Date : 2018-10-19DOI: 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}
Pub Date : 2018-10-19DOI: 10.1002/9781119518068.CH6
H. Kulik
{"title":"MODELING MECHANOCHEMISTRY FROM FIRST PRINCIPLES","authors":"H. Kulik","doi":"10.1002/9781119518068.CH6","DOIUrl":"https://doi.org/10.1002/9781119518068.CH6","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.CH6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44803368","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}
Pub Date : 2018-05-09DOI: 10.1002/9781119518068.CH2
D. Leitner, T. Yamato
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.
{"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}
Pub Date : 2018-01-04DOI: 10.1002/9781119518068.CH3
P. Patrone, A. Dienstfrey
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.
{"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}
Pub Date : 2016-05-06DOI: 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}
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