Journal of Chemical Theory and Computation最新文献_第5页

Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles 慢速集合变量、马尔可夫动力学和过渡态集合的谱图

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2024-09-12 DOI: 10.1021/acs.jctc.4c0042810.1021/acs.jctc.4c00428

Jakub Rydzewski*,

{"title":"Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles","authors":"Jakub Rydzewski*, ","doi":"10.1021/acs.jctc.4c0042810.1021/acs.jctc.4c00428","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c00428https://doi.org/10.1021/acs.jctc.4c00428","url":null,"abstract":"Understanding the behavior of complex molecular systems is a fundamental problem in physical chemistry. To describe the long-time dynamics of such systems, which is responsible for their most informative characteristics, we can identify a few slow collective variables (CVs) while treating the remaining fast variables as thermal noise. This enables us to simplify the dynamics and treat it as diffusion in a free-energy landscape spanned by slow CVs, effectively rendering the dynamics Markovian. Our recent statistical learning technique, spectral map [Rydzewski, J. J. Phys. Chem. Lett. 2023, 14(22), 5216–5220], explores this strategy to learn slow CVs by maximizing a spectral gap of a transition matrix. In this work, we introduce several advancements into our framework, using a high-dimensional reversible folding process of a protein as an example. We implement an algorithm for coarse-graining Markov transition matrices to partition the reduced space of slow CVs kinetically and use it to define a transition state ensemble. We show that slow CVs learned by spectral map closely approach the Markovian limit for an overdamped diffusion. We demonstrate that coordinate-dependent diffusion coefficients only slightly affect the constructed free-energy landscapes. Finally, we present how spectral maps can be used to quantify the importance of features and compare slow CVs with structural descriptors commonly used in protein folding. Overall, we demonstrate that a single slow CV learned by spectral map can be used as a physical reaction coordinate to capture essential characteristics of protein folding.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.jctc.4c00428","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Synergistic Integration of Physical Embedding and Machine Learning Enabling Precise and Reliable Force Field 物理嵌入与机器学习的协同集成实现了精确可靠的力场

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2024-09-12 DOI: 10.1021/acs.jctc.4c00618

Lifeng Xu, Jian Jiang

{"title":"Synergistic Integration of Physical Embedding and Machine Learning Enabling Precise and Reliable Force Field","authors":"Lifeng Xu, Jian Jiang","doi":"10.1021/acs.jctc.4c00618","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c00618","url":null,"abstract":"Machine-learning force fields have achieved significant strides in accurately reproducing the potential energy surface with quantum chemical accuracy. However, this approach still faces several challenges, e.g., extrapolating to uncharted chemical spaces, interpreting long-range electrostatics, and mapping complex macroscopic properties. To address these issues, we advocate for a synergistic integration of physical principles and machine learning techniques within the framework of a physically informed neural network (PINN). This approach involves incorporating physical knowledge into the parameters of the neural network, coupled with an efficient global optimizer, the Tabu-Adam algorithm, proposed in this work to augment optimization under strict physical constraint. We choose the AMOEBA+ force field as the physics-based model for embedding and then train and test it using the diethylene glycol dimethyl ether (DEGDME) data set as a case study. The results reveal a breakthrough in constructing a precise and noise-robust machine learning force field. Utilizing two training sets with hundreds of samples, our model exhibits remarkable generalization and density functional theory (DFT) accuracy in describing molecular interactions and enables a precise prediction of the macroscopic properties such as the diffusion coefficient with minimal cost. This work provides valuable insight into establishing a fundamental framework of the PINN force field.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles 慢速集合变量、马尔可夫动力学和过渡态集合的谱图

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2024-09-12 DOI: 10.1021/acs.jctc.4c00428

Jakub Rydzewski

{"title":"Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles","authors":"Jakub Rydzewski","doi":"10.1021/acs.jctc.4c00428","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c00428","url":null,"abstract":"Understanding the behavior of complex molecular systems is a fundamental problem in physical chemistry. To describe the long-time dynamics of such systems, which is responsible for their most informative characteristics, we can identify a few slow collective variables (CVs) while treating the remaining fast variables as thermal noise. This enables us to simplify the dynamics and treat it as diffusion in a free-energy landscape spanned by slow CVs, effectively rendering the dynamics Markovian. Our recent statistical learning technique, spectral map [Rydzewski, J. J. Phys. Chem. Lett. 2023, 14(22), 5216–5220], explores this strategy to learn slow CVs by maximizing a spectral gap of a transition matrix. In this work, we introduce several advancements into our framework, using a high-dimensional reversible folding process of a protein as an example. We implement an algorithm for coarse-graining Markov transition matrices to partition the reduced space of slow CVs kinetically and use it to define a transition state ensemble. We show that slow CVs learned by spectral map closely approach the Markovian limit for an overdamped diffusion. We demonstrate that coordinate-dependent diffusion coefficients only slightly affect the constructed free-energy landscapes. Finally, we present how spectral maps can be used to quantify the importance of features and compare slow CVs with structural descriptors commonly used in protein folding. Overall, we demonstrate that a single slow CV learned by spectral map can be used as a physical reaction coordinate to capture essential characteristics of protein folding.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Singlet–Triplet Inversion in Triangular Boron Carbon Nitrides 三角形碳氮化硼中的单三反转

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2024-09-12 DOI: 10.1021/acs.jctc.4c00706

Matteo Bedogni, Francesco Di Maiolo

引用次数: 0

Stability and Dynamics of Zeolite-Confined Gold Nanoclusters 沸石束缚金纳米团簇的稳定性和动态性

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2024-09-12 DOI: 10.1021/acs.jctc.4c0097810.1021/acs.jctc.4c00978

Siddharth Sonti, Chenghan Sun, Zekun Chen, Robert Michael Kowalski, Joseph S. Kowalski, Davide Donadio, Surl-Hee Ahn* and Ambarish R. Kulkarni*,

{"title":"Stability and Dynamics of Zeolite-Confined Gold Nanoclusters","authors":"Siddharth Sonti, Chenghan Sun, Zekun Chen, Robert Michael Kowalski, Joseph S. Kowalski, Davide Donadio, Surl-Hee Ahn* and Ambarish R. Kulkarni*, ","doi":"10.1021/acs.jctc.4c0097810.1021/acs.jctc.4c00978","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c00978https://doi.org/10.1021/acs.jctc.4c00978","url":null,"abstract":"Nanoengineered metal@zeolite materials have recently emerged as a promising class of catalysts for several industrially relevant reactions. These materials, which consist of small transition metal nanoclusters confined within three-dimensional zeolite pores, are interesting because they show higher stability and better sintering resistance under reaction conditions. While several such hybrid catalysts have been reported experimentally, key questions such as the impact of the zeolite frameworks on the properties of the metal clusters are not well understood. To address such knowledge gaps, in this study, we report a robust and transferable machine learning-based potential (MLP) that is capable of describing the structure, stability, and dynamics of zeolite-confined gold nanoclusters. Specifically, we show that the resulting MLP maintains ab initio accuracy across a range of temperatures (300–1000 K) and can be used to investigate time scales (>10 ns), length scales (ca. 10,000 atoms), and phenomena (e.g., ensemble-averaged stability and diffusivity) that are typically inaccessible using density functional theory (DFT). Taken together, this study represents an important step in enabling the rational theory-guided design of metal@zeolite catalysts.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.jctc.4c00978","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations 利用物理信息主动学习加速量子化学模拟

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2024-09-12 DOI: 10.1021/acs.jctc.4c00821

Yi-Fan Hou, Lina Zhang, Quanhao Zhang, Fuchun Ge, Pavlo O. Dral

引用次数: 0

Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations 利用物理信息主动学习加速量子化学模拟

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2024-09-12 DOI: 10.1021/acs.jctc.4c0082110.1021/acs.jctc.4c00821

Yi-Fan Hou, Lina Zhang, Quanhao Zhang, Fuchun Ge and Pavlo O. Dral*,

引用次数: 0

Synergistic Integration of Physical Embedding and Machine Learning Enabling Precise and Reliable Force Field 物理嵌入与机器学习的协同集成实现了精确可靠的力场

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2024-09-12 DOI: 10.1021/acs.jctc.4c0061810.1021/acs.jctc.4c00618

Lifeng Xu, and , Jian Jiang*,

{"title":"Synergistic Integration of Physical Embedding and Machine Learning Enabling Precise and Reliable Force Field","authors":"Lifeng Xu,  and , Jian Jiang*, ","doi":"10.1021/acs.jctc.4c0061810.1021/acs.jctc.4c00618","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c00618https://doi.org/10.1021/acs.jctc.4c00618","url":null,"abstract":"Machine-learning force fields have achieved significant strides in accurately reproducing the potential energy surface with quantum chemical accuracy. However, this approach still faces several challenges, e.g., extrapolating to uncharted chemical spaces, interpreting long-range electrostatics, and mapping complex macroscopic properties. To address these issues, we advocate for a synergistic integration of physical principles and machine learning techniques within the framework of a physically informed neural network (PINN). This approach involves incorporating physical knowledge into the parameters of the neural network, coupled with an efficient global optimizer, the Tabu-Adam algorithm, proposed in this work to augment optimization under strict physical constraint. We choose the AMOEBA+ force field as the physics-based model for embedding and then train and test it using the diethylene glycol dimethyl ether (DEGDME) data set as a case study. The results reveal a breakthrough in constructing a precise and noise-robust machine learning force field. Utilizing two training sets with hundreds of samples, our model exhibits remarkable generalization and density functional theory (DFT) accuracy in describing molecular interactions and enables a precise prediction of the macroscopic properties such as the diffusion coefficient with minimal cost. This work provides valuable insight into establishing a fundamental framework of the PINN force field.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Stability and Dynamics of Zeolite-Confined Gold Nanoclusters 沸石束缚金纳米团簇的稳定性和动态性

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2024-09-12 DOI: 10.1021/acs.jctc.4c00978

Siddharth Sonti, Chenghan Sun, Zekun Chen, Robert Michael Kowalski, Joseph S. Kowalski, Davide Donadio, Surl-Hee Ahn, Ambarish R. Kulkarni

{"title":"Stability and Dynamics of Zeolite-Confined Gold Nanoclusters","authors":"Siddharth Sonti, Chenghan Sun, Zekun Chen, Robert Michael Kowalski, Joseph S. Kowalski, Davide Donadio, Surl-Hee Ahn, Ambarish R. Kulkarni","doi":"10.1021/acs.jctc.4c00978","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c00978","url":null,"abstract":"Nanoengineered metal@zeolite materials have recently emerged as a promising class of catalysts for several industrially relevant reactions. These materials, which consist of small transition metal nanoclusters confined within three-dimensional zeolite pores, are interesting because they show higher stability and better sintering resistance under reaction conditions. While several such hybrid catalysts have been reported experimentally, key questions such as the impact of the zeolite frameworks on the properties of the metal clusters are not well understood. To address such knowledge gaps, in this study, we report a robust and transferable machine learning-based potential (MLP) that is capable of describing the structure, stability, and dynamics of zeolite-confined gold nanoclusters. Specifically, we show that the resulting MLP maintains ab initio accuracy across a range of temperatures (300–1000 K) and can be used to investigate time scales (>10 ns), length scales (ca. 10,000 atoms), and phenomena (e.g., ensemble-averaged stability and diffusivity) that are typically inaccessible using density functional theory (DFT). Taken together, this study represents an important step in enabling the rational theory-guided design of metal@zeolite catalysts.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Nonadiabatic Hydrogen Tunneling Dynamics for Multiple Proton Transfer Processes with Generalized Nuclear-Electronic Orbital Multistate Density Functional Theory 利用广义核电子轨道多态密度泛函理论研究多质子转移过程的非绝热氢隧道动力学

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2024-09-11 DOI: 10.1021/acs.jctc.4c0073710.1021/acs.jctc.4c00737

Joseph A. Dickinson, and , Sharon Hammes-Schiffer*,

{"title":"Nonadiabatic Hydrogen Tunneling Dynamics for Multiple Proton Transfer Processes with Generalized Nuclear-Electronic Orbital Multistate Density Functional Theory","authors":"Joseph A. Dickinson,  and , Sharon Hammes-Schiffer*, ","doi":"10.1021/acs.jctc.4c0073710.1021/acs.jctc.4c00737","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c00737https://doi.org/10.1021/acs.jctc.4c00737","url":null,"abstract":"Proton transfer and hydrogen tunneling play key roles in many processes of chemical and biological importance. The generalized nuclear-electronic orbital multistate density functional theory (NEO-MSDFT) method was developed in order to capture hydrogen tunneling effects in systems involving the transfer and tunneling of one or more protons. The generalized NEO-MSDFT method treats the transferring protons quantum mechanically on the same level as the electrons and obtains the delocalized vibronic states associated with hydrogen tunneling by mixing localized NEO-DFT states in a nonorthogonal configuration interaction scheme. Herein, we present the derivation and implementation of analytical gradients for the generalized NEO-MSDFT vibronic state energies and the nonadiabatic coupling vectors between these vibronic states. We use this methodology to perform adiabatic and nonadiabatic dynamics simulations of the double proton transfer reactions in the formic acid dimer and the heterodimer of formamidine and formic acid. The generalized NEO-MSDFT method is shown to capture the strongly coupled synchronous or asynchronous tunneling of the two protons in these processes. Inclusion of vibronically nonadiabatic effects is found to significantly impact the double proton transfer dynamics. This work lays the foundation for a variety of nonadiabatic dynamics simulations of multiple proton transfer systems, such as proton relays and hydrogen-bonding networks.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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