{"title":"甲酸二聚体中受约束的核-电子轨道分子动力学的有限温度双质子转移:核量子离域的低障碍和提高速率。","authors":"Yuzhe Zhang,Zhe Liu,Yang Yang","doi":"10.1021/acs.jctc.5c00532","DOIUrl":null,"url":null,"abstract":"Proton transfer plays a crucial role in various chemical and biological processes, yet accurately and efficiently describing such reactions remains challenging due to nuclear quantum effects (NQEs). In this work, we employ constrained nuclear-electronic orbital molecular dynamics (CNEO-MD), a method that inherently incorporates NQEs into classical dynamics to investigate double proton transfer in the formic acid dimer (FAD). Leveraging machine learning techniques, we efficiently construct free energy landscapes with NQEs at finite temperatures and obtain transition state theory (TST) rates. Transmission coefficients are further evaluated using flux-side correlation functions to obtain corrected rates that account for recrossing effects. CNEO-MD predicts significantly lowered free energy barriers and enhanced rates compared to conventional DFT-based ab initio molecular dynamics (AIMD), agreeing qualitatively with previous path-integral simulations. Additionally, we explore solvent effects using CNEO-MD with a dielectric continuum solvent model and find the change of the solvent model makes negligible effects. Our findings demonstrate the effectiveness of CNEO-MD in describing proton transfer reactions at finite temperatures and highlight its potential for applications in more complex systems.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"78 1","pages":""},"PeriodicalIF":5.5000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Finite-Temperature Double Proton Transfer in Formic Acid Dimer via Constrained Nuclear-Electronic Orbital Molecular Dynamics: Lower Barriers and Enhanced Rates from Nuclear Quantum Delocalization.\",\"authors\":\"Yuzhe Zhang,Zhe Liu,Yang Yang\",\"doi\":\"10.1021/acs.jctc.5c00532\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Proton transfer plays a crucial role in various chemical and biological processes, yet accurately and efficiently describing such reactions remains challenging due to nuclear quantum effects (NQEs). In this work, we employ constrained nuclear-electronic orbital molecular dynamics (CNEO-MD), a method that inherently incorporates NQEs into classical dynamics to investigate double proton transfer in the formic acid dimer (FAD). Leveraging machine learning techniques, we efficiently construct free energy landscapes with NQEs at finite temperatures and obtain transition state theory (TST) rates. Transmission coefficients are further evaluated using flux-side correlation functions to obtain corrected rates that account for recrossing effects. CNEO-MD predicts significantly lowered free energy barriers and enhanced rates compared to conventional DFT-based ab initio molecular dynamics (AIMD), agreeing qualitatively with previous path-integral simulations. Additionally, we explore solvent effects using CNEO-MD with a dielectric continuum solvent model and find the change of the solvent model makes negligible effects. Our findings demonstrate the effectiveness of CNEO-MD in describing proton transfer reactions at finite temperatures and highlight its potential for applications in more complex systems.\",\"PeriodicalId\":45,\"journal\":{\"name\":\"Journal of Chemical Theory and Computation\",\"volume\":\"78 1\",\"pages\":\"\"},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2025-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Chemical Theory and Computation\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.jctc.5c00532\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Theory and Computation","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jctc.5c00532","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Finite-Temperature Double Proton Transfer in Formic Acid Dimer via Constrained Nuclear-Electronic Orbital Molecular Dynamics: Lower Barriers and Enhanced Rates from Nuclear Quantum Delocalization.
Proton transfer plays a crucial role in various chemical and biological processes, yet accurately and efficiently describing such reactions remains challenging due to nuclear quantum effects (NQEs). In this work, we employ constrained nuclear-electronic orbital molecular dynamics (CNEO-MD), a method that inherently incorporates NQEs into classical dynamics to investigate double proton transfer in the formic acid dimer (FAD). Leveraging machine learning techniques, we efficiently construct free energy landscapes with NQEs at finite temperatures and obtain transition state theory (TST) rates. Transmission coefficients are further evaluated using flux-side correlation functions to obtain corrected rates that account for recrossing effects. CNEO-MD predicts significantly lowered free energy barriers and enhanced rates compared to conventional DFT-based ab initio molecular dynamics (AIMD), agreeing qualitatively with previous path-integral simulations. Additionally, we explore solvent effects using CNEO-MD with a dielectric continuum solvent model and find the change of the solvent model makes negligible effects. Our findings demonstrate the effectiveness of CNEO-MD in describing proton transfer reactions at finite temperatures and highlight its potential for applications in more complex systems.
期刊介绍:
The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.