Journal of Chemical Physics最新文献_第8页

Considerations in the use of machine learning force fields for free energy calculations. 在自由能计算中使用机器学习力场的考虑。

IF 3.1 2区化学

Journal of Chemical Physics Pub Date : 2025-05-07 DOI: 10.1063/5.0252043

Orlando A Mendible-Barreto, Jonathan K Whitmer, Yamil J Colón

{"title":"Considerations in the use of machine learning force fields for free energy calculations.","authors":"Orlando A Mendible-Barreto, Jonathan K Whitmer, Yamil J Colón","doi":"10.1063/5.0252043","DOIUrl":"https://doi.org/10.1063/5.0252043","url":null,"abstract":"Machine learning force fields (MLFFs) promise to accurately describe the potential energy surface of molecules at the ab initio level of theory with improved computational efficiency. Within MLFFs, equivariant graph neural networks (EQNNs) have shown great promise in accuracy and performance and are the focus of this work. The capability of EQNNs to recover free energy surfaces (FES) remains to be thoroughly investigated. In this work, we investigate the impact of collective variables (CVs) distribution within the training data on the accuracy of EQNNs predicting the FES of butane and alanine dipeptide. A generalizable workflow is presented in which training configurations are generated with classical molecular dynamics simulations, and energies and forces are obtained with ab initio calculations. We evaluate how bond and angle constraints in the training data influence the accuracy of EQNN force fields in reproducing the FES of the molecules at both classical and ab initio levels of theory. Results indicate that the model's accuracy is unaffected by the distribution of sampled CVs during training, given that the training data includes configurations from characteristic regions of the system's FES. However, when the training data is obtained from classical simulations, the EQNN struggles to extrapolate the free energy for configurations with high free energy. In contrast, models trained with the same configurations on ab initio data show improved extrapolation accuracy. The findings underscore the difficulties in creating a comprehensive training dataset for EQNNs to predict FESs and highlight the importance of prior knowledge of the system's FES.","PeriodicalId":15313,"journal":{"name":"Journal of Chemical Physics","volume":"162 17","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143966145","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

Elastic and exchanged atom-molecule collisions in 4He-4He-X (X = 40K, 85Rb, 133Cs) systems at low energy. 低能4He-4He-X （X = 40K, 85Rb, 133c）体系的弹性和交换原子-分子碰撞。

IF 3.1 2区化学

Journal of Chemical Physics Pub Date : 2025-05-07 DOI: 10.1063/5.0269552

Huan Zhu, Ning-Ning Gao, Hui-Li Han

引用次数: 0

DNA relaxation dynamics in crowded environments: Influence of PEG molecular weight and viscosity. 拥挤环境中的DNA弛豫动力学：PEG分子量和粘度的影响。

IF 3.1 2区化学

Journal of Chemical Physics Pub Date : 2025-05-07 DOI: 10.1063/5.0257786

Yunosuke Fuji, Yota Nojiri, Yuuta Moriyama, Toshiyuki Mitsui

引用次数: 0

Nonmonotonic concentration dependence of the self-diffusion coefficient of surfactants in wormlike micellar solutions. 蠕虫状胶束溶液中表面活性剂自扩散系数的非单调浓度依赖性。

IF 3.1 2区化学

Journal of Chemical Physics Pub Date : 2025-05-07 DOI: 10.1063/5.0266470

Yusuke Koide, Takato Ishida, Takashi Uneyama, Yuichi Masubuchi

引用次数: 0

Superfluidity of quasi-2D 4He droplets on graphite. 石墨上准二维4He液滴的超流动性。

IF 3.1 2区化学

Journal of Chemical Physics Pub Date : 2025-05-07 DOI: 10.1063/5.0270881

K M Kolevski, M Boninsegni

引用次数: 0

Dynamics of an active chiral polymer in shear flow. 一种活性手性聚合物在剪切流动中的动力学。

IF 3.1 2区化学

Journal of Chemical Physics Pub Date : 2025-05-07 DOI: 10.1063/5.0268723

A Gülce Bayram, Luca Biancofiore, Hartmut Löwen

引用次数: 0

Precision, intelligence, and a new paradigm for chemical research. 精确，智能，化学研究的新范式。

IF 3.1 2区化学

Journal of Chemical Physics Pub Date : 2025-05-07 DOI: 10.1063/5.0262187

Shuo Feng, Jun Jiang, Zhenyu Li

{"title":"Precision, intelligence, and a new paradigm for chemical research.","authors":"Shuo Feng, Jun Jiang, Zhenyu Li","doi":"10.1063/5.0262187","DOIUrl":"https://doi.org/10.1063/5.0262187","url":null,"abstract":"Chemists have long struggled to precisely regulate and create substances, often relying on trial-and-error methods that are inefficient for complex, high-dimensional research challenges. However, recent advancements in computational and experimental techniques, particularly those with artificial intelligence (AI), are providing new avenues for precision and intelligent chemistry. This perspective highlights the synergistic integration of accurate theoretical simulations, advanced experimental characterization, and AI-driven models, creating a closed-loop system to accelerate chemical discovery and material design. At the core of this framework is an iterative process: precise computational and experimental data lead to advanced intelligent models, which guide the design of optimized reaction parameters or chemical components, and direct robotic platforms that perform reproducible, high-throughput experiments. These experimental data, in turn, provide continuous feedback to refine intelligent models, ultimately enabling precise control of reaction conditions and material properties. To fully realize this vision, we advocate the development of key infrastructures: a multidisciplinary, multimodal, and standardized AI-ready chemical database as a data foundation; a knowledge and logic-enhanced large chemical model for intelligent prediction and design; distributed, full-process robotic laboratories for automated experimentation; and a cloud platform for resource sharing and collaboration. Together, these components constitute a vision for robotic chemist cloud facilities, which will empower researchers with unparalleled capabilities to seamlessly integrate precision and intelligence. This integrated approach promises to accelerate discovery and represents a paradigm shift in chemical research.","PeriodicalId":15313,"journal":{"name":"Journal of Chemical Physics","volume":"162 17","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144020171","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

On the origin of the breakloose friction force. 关于断裂摩擦力的来源。

IF 3.1 2区化学

Journal of Chemical Physics Pub Date : 2025-05-07 DOI: 10.1063/5.0266065

T Tada, B N J Persson

引用次数: 0

Analysis of variants of non-adiabatic ring polymer molecular dynamics for calculating excited state dynamics. 计算激发态动力学的非绝热环状聚合物分子动力学变体分析。

IF 3.1 2区化学

Journal of Chemical Physics Pub Date : 2025-05-07 DOI: 10.1063/5.0255457

Ziying Cao, Joshua S Kretchmer

引用次数: 0

Low energy electron induced fragmentation of hot asparagine and aspartic acid molecules. 低能电子诱导热天冬酰胺和天冬氨酸分子碎裂。

IF 3.1 2区化学

Journal of Chemical Physics Pub Date : 2025-05-07 DOI: 10.1063/5.0254082

Janina Kopyra, Aleksandr Bancer, Hassan Abdoul-Carime

{"title":"Low energy electron induced fragmentation of hot asparagine and aspartic acid molecules.","authors":"Janina Kopyra, Aleksandr Bancer, Hassan Abdoul-Carime","doi":"10.1063/5.0254082","DOIUrl":"https://doi.org/10.1063/5.0254082","url":null,"abstract":"Asparagine (Asn) and aspartic acid (Asp) are not only amino acids found in proteins but also building blocks for synthesis of eco-friendly polymers with techniques such those in which electrons play a role. In this contribution, we present a comparative study of dissociative electron attachment (DEA) to Asn and Asp under gas-phase conditions by means of DEA spectroscopy. In contrast to a number of amino acids studied so far, the formation of the dehydrogenated parent anion, (M - H)-, at about 1.2 eV from both investigated compounds is not the most intense dissociation channel. On the other hand, prominent negative ion resonances are observed peaking below 0.5 eV, resulting in the formation of fragment anions generated from a loss of a neutral -COOH group or neutral H2O2 molecule or leading to the production of HCOO- from both asparagine and aspartic acid. It should be stressed here that the surrogation of one hydroxyl group by an amino group in aspartic acid, which results in the formation of asparagine, sensitizes the latter compound for the loss of an entire neutral carboxyl group. Indeed, the formation of the (Asn - COOH)- anion from DEA to asparagine is more efficient by about an order of magnitude than the formation of the (Asp - COOH)- anion from DEA to aspartic acid.","PeriodicalId":15313,"journal":{"name":"Journal of Chemical Physics","volume":"162 17","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002547","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