1
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Wang J, Hei H, Zheng Y, Zhang H, Ye H. Five-Site Water Models for Ice and Liquid Water Generated by a Series-Parallel Machine Learning Strategy. J Chem Theory Comput 2024; 20:7533-7545. [PMID: 39133036 DOI: 10.1021/acs.jctc.4c00440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Icing, a common natural phenomenon, always originates from a molecule. Molecular simulation is crucial for understanding the relevant process but still faces a great challenge in obtaining a uniform and accurate description of ice and liquid water with limited model parameters. Here, we propose a series-parallel machine learning (ML) approach consisting of a classification back-propagation neural network (BPNN), parallel regression BPNNs, and a genetic algorithm to establish conventional TIP5P-BG and temperature-dependent TIP5P-BGT models. The established water models exhibit a comprehensive balance among the crucial physical properties (melting point, density, vaporization enthalpy, self-diffusion coefficient, and viscosity) with mean absolute percentage errors of 2.65 and 2.40%, respectively, and excellent predictive performance on the related properties of liquid water. For ice, the simulation results on the critical nucleus size and growth rate are in good accordance with experiments. This work offers a powerful molecular model for phase transition and icing in nanoconfinement and a construction strategy for a complex molecular model in the extreme case.
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Affiliation(s)
- Jian Wang
- International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, P. R. China
| | - Haitao Hei
- International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, P. R. China
| | - Yonggang Zheng
- International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, P. R. China
- DUT-BSU Joint Institute, Dalian University of Technology, Dalian 116024, P. R. China
| | - Hongwu Zhang
- International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, P. R. China
| | - Hongfei Ye
- International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, P. R. China
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2
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Ma J, Majmudar A, Tian B. Bridging the Gap-Thermofluidic Designs for Precision Bioelectronics. Adv Healthc Mater 2024; 13:e2302431. [PMID: 37975642 DOI: 10.1002/adhm.202302431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/22/2023] [Indexed: 11/19/2023]
Abstract
Bioelectronics, the merging of biology and electronics, can monitor and modulate biological behaviors across length and time scales with unprecedented capability. Current bioelectronics research largely focuses on devices' mechanical properties and electronic designs. However, the thermofluidic control is often overlooked, which is noteworthy given the discipline's importance in almost all bioelectronics processes. It is believed that integrating thermofluidic designs into bioelectronics is essential to align device precision with the complexity of biofluids and biological structures. This perspective serves as a mini roadmap for researchers in both fields to introduce key principles, applications, and challenges in both bioelectronics and thermofluids domains. Important interdisciplinary opportunities for the development of future healthcare devices and precise bioelectronics will also be discussed.
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Affiliation(s)
- Jingcheng Ma
- The James Franck Institute, University of Chicago, Chicago, IL, 60637, USA
| | - Aman Majmudar
- The College, University of Chicago, Chicago, IL, 60637, USA
| | - Bozhi Tian
- The James Franck Institute, University of Chicago, Chicago, IL, 60637, USA
- Department of Chemistry, University of Chicago, Chicago, IL, 60637, USA
- The Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, 60637, USA
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3
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Lin M, Xiong Z, Cao H. Bridging classical nucleation theory and molecular dynamics simulation for homogeneous ice nucleation. J Chem Phys 2024; 161:084504. [PMID: 39206829 DOI: 10.1063/5.0216645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 08/14/2024] [Indexed: 09/04/2024] Open
Abstract
Water freezing, initiated by ice nucleation, occurs widely in nature, ranging from cellular to global phenomena. Ice nucleation has been experimentally proven to require the formation of a critical ice nucleus, consistent with classical nucleation theory (CNT). However, the accuracy of CNT quantitative predictions of critical cluster sizes and nucleation rates has never been verified experimentally. In this study, we circumvent this difficulty by using molecular dynamics (MD) simulation. The physical properties of water/ice for CNT predictions, including density, chemical potential difference, and diffusion coefficient, are independently obtained using MD simulation, whereas the calculation of interfacial free energy is based on thermodynamic assumptions of CNT, including capillarity approximation among others. The CNT predictions are compared to the MD evaluations of brute-force simulations and forward flux sampling methods. We find that the CNT and MD predicted critical cluster sizes are consistent, and the CNT predicted nucleation rates are higher than the MD predicted values within three orders of magnitude. We also find that the ice crystallized from supercooled water is stacking-disordered ice with a stacking of cubic and hexagonal ices in four representative types of stacking. The prediction discrepancies in nucleation rate mainly arise from the stacking-disordered ice structure, the asphericity of ice cluster, the uncertainty of ice-water interfacial free energy, and the kinetic attachment rate. Our study establishes a relation between CNT and MD to predict homogeneous ice nucleation.
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Affiliation(s)
- Min Lin
- Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Zhewen Xiong
- Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Haishan Cao
- Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, People's Republic of China
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4
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Malosso C, Manko N, Izzo MG, Baroni S, Hassanali A. Evidence of ferroelectric features in low-density supercooled water from ab initio deep neural-network simulations. Proc Natl Acad Sci U S A 2024; 121:e2407295121. [PMID: 39083416 PMCID: PMC11317578 DOI: 10.1073/pnas.2407295121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 06/30/2024] [Indexed: 08/02/2024] Open
Abstract
Over the last decade, an increasing body of evidence has emerged, supporting the existence of a metastable liquid-liquid critical point in supercooled water whereby two distinct liquid phases of different densities coexist. Analyzing long molecular dynamics simulations performed using deep neural-network force fields trained to accurate quantum mechanical data, we demonstrate that the low-density liquid phase displays a strong propensity toward spontaneous polarization, as witnessed by large and long-lived collective dipole fluctuations. Our findings suggest that the dynamical stability of the low-density phase, and hence the transition from high-density to low-density liquid, is triggered by a collective process involving an accumulation of rotational angular jumps, which could ignite large dipole fluctuations. This dynamical transition involves subtle changes in the electronic polarizability of water molecules which affects their rotational mobility within the two phases. These findings hold the potential for catalyzing activity in the search for dielectric-based probes of the putative second critical point.
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Affiliation(s)
- Cesare Malosso
- Scuola Internazionale Superiore di Studi Avanzati, Trieste34136, Italy
| | - Natalia Manko
- Condensed Matter and Statistical Physics (CMSP), The Abdus Salam Centre for Theoretical Physics, Trieste34151, Italy
| | - Maria Grazia Izzo
- Scuola Internazionale Superiore di Studi Avanzati, Trieste34136, Italy
| | - Stefano Baroni
- Scuola Internazionale Superiore di Studi Avanzati, Trieste34136, Italy
- Consiglio Nazionale delle Ricerche-Istituto Officina dei Materiali, Scuola Internazionale Superiore di Studi Avanzati Unit, Trieste34136, Italy
| | - Ali Hassanali
- Condensed Matter and Statistical Physics (CMSP), The Abdus Salam Centre for Theoretical Physics, Trieste34151, Italy
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5
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Sedano LF, Blazquez S, Vega C. Accuracy limit of non-polarizable four-point water models: TIP4P/2005 vs OPC. Should water models reproduce the experimental dielectric constant? J Chem Phys 2024; 161:044505. [PMID: 39046346 DOI: 10.1063/5.0211871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 06/30/2024] [Indexed: 07/25/2024] Open
Abstract
The last generation of four center non-polarizable models of water can be divided into two groups: those reproducing the dielectric constant of water, as OPC, and those significantly underestimating its value, as TIP4P/2005. To evaluate the global performance of OPC and TIP4P/2005, we shall follow the test proposed by Vega and Abascal in 2011 evaluating about 40 properties to fairly address this comparison. The liquid-vapor and liquid-solid equilibria are computed, as well as the heat capacities, isothermal compressibilities, surface tensions, densities of different ice polymorphs, the density maximum, equations of state at high pressures, and transport properties. General aspects of the phase diagram are considered by comparing the ratios of different temperatures (namely, the temperature of maximum density, the melting temperature of hexagonal ice, and the critical temperature). The final scores are 7.2 for TIP4P/2005 and 6.3 for OPC. The results of this work strongly suggest that we have reached the limit of what can be achieved with non-polarizable models of water and that the attempt to reproduce the experimental dielectric constant deteriorates the global performance of the water force field. The reason is that the dielectric constant depends on two surfaces (potential energy and dipole moment surfaces), whereas in the absence of an electric field, all properties can be determined simply from just one surface (the potential energy surface). The consequences of the choice of the water model in the modeling of electrolytes in water are also discussed.
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Affiliation(s)
- L F Sedano
- Departamento de Química Física, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - S Blazquez
- Departamento de Química Física, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - C Vega
- Departamento de Química Física, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
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6
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Zhang P, Feng M, Xu X. Double-Layer Distribution of Hydronium and Hydroxide Ions in the Air-Water Interface. ACS PHYSICAL CHEMISTRY AU 2024; 4:336-346. [PMID: 39069983 PMCID: PMC11274287 DOI: 10.1021/acsphyschemau.3c00076] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 07/30/2024]
Abstract
The acid-base nature of the aqueous interface has long been controversial. Most macroscopic experiments suggest that the air-water interface is basic based on the detection of negative charges at the interface that indicates the enrichment of hydroxides (OH-), whereas microscopic studies mostly support the acidic air-water interface with the observation of hydronium (H3O+) accumulation in the top layer of the interface. It is crucial to clarify the interfacial preference of OH- and H3O+ ions for rationalizing the debate. In this work, we perform deep potential molecular dynamics simulations to investigate the preferential distribution of OH- and H3O+ ions at the aqueous interfaces. The neural network potential energy surface is trained based on density functional theory calculations with the SCAN functional, which can accurately describe the diffusion of these two ions both in the interface and in the bulk water. In contrast to the previously reported single ion enrichment, we show that both OH- and H3O+ surprisingly prefer to accumulate in interfaces but at different interfacial depths, rendering a double-layer ionic distribution within ∼1 nm near the Gibbs dividing surface. The H3O+ preferentially resides in the topmost layer of the interface, but the OH-, which is enriched in the deeper interfacial layer, has a higher equilibrium concentration due to the more negative free energy of interfacial stabilization [-0.90 (OH-) vs -0.56 (H3O+) kcal/mol]. The present finding of the ionic double-layer distribution may qualitatively offer a self-consistent explanation for the long-term controversy about the acid-base nature of the air-water interface.
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Affiliation(s)
- Pengchao Zhang
- Center
for Combustion Energy, Department of Energy and Power Engineering,
and Key Laboratory for Thermal Science and Power Engineering of Ministry
of Education, Tsinghua University, Beijing 100084, China
| | - Muye Feng
- School
of Mechanical and Power Engineering, Nanjing
Tech University, Nanjing 211816, China
| | - Xuefei Xu
- Center
for Combustion Energy, Department of Energy and Power Engineering,
and Key Laboratory for Thermal Science and Power Engineering of Ministry
of Education, Tsinghua University, Beijing 100084, China
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7
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Zhang P, Chen C, Feng M, Sun C, Xu X. Hydroxide and Hydronium Ions Modulate the Dynamic Evolution of Nitrogen Nanobubbles in Water. J Am Chem Soc 2024; 146:19537-19546. [PMID: 38949461 DOI: 10.1021/jacs.4c06641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
It has been widely recognized that the pH environment influences the nanobubble dynamics and hydroxide ions adsorbed on the surface may be responsible for the long-term survival of the nanobubbles. However, understanding the distribution of hydronium and hydroxide ions in the vicinity of a bulk nanobubble surface at a microscopic scale and the consequent impact of these ions on the nanobubble behavior remains a challenging endeavor. In this study, we carried out deep potential molecular dynamics simulations to explore the behavior of a nitrogen nanobubble under neutral, acidic, and alkaline conditions and the inherent mechanism, and we also conducted a theoretical thermodynamic and dynamic analysis to address constraints related to simulation duration. Our simulations and theoretical analyses demonstrate a trend of nanobubble dissolution similar to that observed experimentally, emphasizing the limited dissolution of bulk nanobubbles in alkaline conditions, where hydroxide ions tend to reside slightly farther from the nanobubble surface than hydronium ions, forming more stable hydrogen bond networks that shield the nanobubble from dissolution. In acidic conditions, the hydronium ions preferentially accumulating at the nanobubble surface in an orderly manner drive nanobubble dissolution to increase the entropy of the system, and the dissolved nitrogen molecules further strengthen the hydrogen bond networks of systems by providing a hydrophobic environment for hydronium ions, suggesting both entropy and enthalpy effects contribute to the instability of nanobubbles under acidic conditions. These results offer fresh insights into the double-layer distribution of hydroxide and hydronium near the nitrogen-water interface that influences the dynamic behavior of bulk nanobubbles.
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Affiliation(s)
- Pengchao Zhang
- Center for Combustion Energy, Department of Energy and Power Engineering, and Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Tsinghua University, Beijing 100084, China
| | - Changsheng Chen
- Center for Combustion Energy, Department of Energy and Power Engineering, and Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Tsinghua University, Beijing 100084, China
| | - Muye Feng
- School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Chao Sun
- Center for Combustion Energy, Department of Energy and Power Engineering, and Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Tsinghua University, Beijing 100084, China
- New Cornerstone Science Laboratory, Tsinghua University, Beijing 100084, China
- Department of Engineering Mechanics, School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
| | - Xuefei Xu
- Center for Combustion Energy, Department of Energy and Power Engineering, and Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Tsinghua University, Beijing 100084, China
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8
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Zhang P, Gardini AT, Xu X, Parrinello M. Intramolecular and Water Mediated Tautomerism of Solvated Glycine. J Chem Inf Model 2024; 64:3599-3604. [PMID: 38620066 DOI: 10.1021/acs.jcim.4c00273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Understanding tautomerism and characterizing solvent effects on the dynamic processes pose significant challenges. Using enhanced-sampling molecular dynamics based on state-of-the-art deep learning potentials, we investigated the tautomeric equilibria of glycine in water. We observed that the tautomerism between neutral and zwitterionic glycine can occur through both intramolecular and intermolecular proton transfers. The latter proceeds involving a contact anionic-glycine-hydronium ion pair or separate cationic-glycine-hydroxide ion pair. These pathways with comparable barriers contribute almost equally to the reaction flux.
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Affiliation(s)
- Pengchao Zhang
- Center for Combustion Energy, Department of Energy and Power Engineering, and Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Tsinghua University, Beijing 100084, China
- Atomistic Simulations, Italian Institute of Technology, Genova 16152, Italy
| | - Axel Tosello Gardini
- Atomistic Simulations, Italian Institute of Technology, Genova 16152, Italy
- Department of Materials Science, Università di Milano-Bicocca, 20126 Milano, Italy
| | - Xuefei Xu
- Center for Combustion Energy, Department of Energy and Power Engineering, and Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Tsinghua University, Beijing 100084, China
| | - Michele Parrinello
- Atomistic Simulations, Italian Institute of Technology, Genova 16152, Italy
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9
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Soni A, Patey GN. Using machine learning with atomistic surface and local water features to predict heterogeneous ice nucleation. J Chem Phys 2024; 160:124501. [PMID: 38530008 DOI: 10.1063/5.0177706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 03/04/2024] [Indexed: 03/27/2024] Open
Abstract
Heterogeneous ice nucleation (HIN) has applications in climate science, nanotechnology, and cryopreservation. Ice nucleation on the earth's surface or in the atmosphere usually occurs heterogeneously involving foreign substrates, known as ice nucleating particles (INPs). Experiments identify good INPs but lack sufficient microscopic resolution to answer the basic question: What makes a good INP? We employ molecular dynamics (MD) simulations in combination with machine learning (ML) to address this question. Often, the large amount of computational cost required to cross the nucleation barrier and observe HIN in MD simulations is a practical limitation. We use information obtained from short MD simulations of atomistic surface and water models to predict the likelihood of HIN. We consider 153 atomistic substrates with some surfaces differing in elemental composition and others only in terms of lattice parameters, surface morphology, or surface charges. A range of water features near the surface (local) are extracted from short MD simulations over a time interval (≤300 ns) where ice nucleation has not initiated. Three ML classification models, Random Forest (RF), support vector machine, and Gaussian process classification are considered, and the accuracies achieved by all three approaches lie within their statistical uncertainties. Including local water features is essential for accurate prediction. The accuracy of our best RF classification model obtained including both surface and local water features is 0.89 ± 0.05. A similar accuracy can be achieved including only local water features, suggesting that the important surface properties are largely captured by the local water features. Some important features identified by ML analysis are local icelike structures, water density and polarization profiles perpendicular to the surface, and the two-dimensional lattice match to ice. We expect that this work, with its strong focus on realistic surface models, will serve as a guide to the identification or design of substrates that can promote or discourage ice nucleation.
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Affiliation(s)
- Abhishek Soni
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - G N Patey
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
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10
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Zhong S, Shi Z, Zhang B, Wen Z, Chen L. Homogeneous water vapor condensation with a deep neural network potential model. J Chem Phys 2024; 160:124303. [PMID: 38516980 DOI: 10.1063/5.0189448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/03/2024] [Indexed: 03/23/2024] Open
Abstract
Molecular-level nucleation has not been clearly understood due to the complexity of multi-body potentials and the stochastic, rare nature of the process. This work utilizes molecular dynamics (MD) simulations, incorporating a first-principles-based deep neural network (DNN) potential model, to investigate homogeneous water vapor condensation. The nucleation rates and critical nucleus sizes predicted by the DNN model are compared against commonly used semi-empirical models, namely extended simple point charge (SPC/E), TIP4P, and OPC, in addition to classical nucleation theory (CNT). The nucleation rates from the DNN model are comparable with those from the OPC model yet surpass the rates from the SPC/E and TIP4P models, a discrepancy that could mainly arise from the overestimated bulk free energy by SPC/E and TIP4P. The surface free energy predicted by CNT is lower than that in MD simulations, while its bulk free energy is higher than that in MD simulations, irrespective of the potential model used. Further analysis of cluster properties with the DNN model unveils pronounced variations of O-H bond length and H-O-H bond angle, along with averaged bond lengths and angles that are enlarged during embryonic cluster formation. Properties such as cluster surface free energy and liquid-to-vapor density transition profiles exhibit significant deviations from CNT assumptions.
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Affiliation(s)
- Shenghui Zhong
- International Innovation Institute, Beihang University, Hangzhou 311115, China
| | - Zheyu Shi
- International Innovation Institute, Beihang University, Hangzhou 311115, China
- College of Science, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Bin Zhang
- International Innovation Institute, Beihang University, Hangzhou 311115, China
| | - Zhengcheng Wen
- College of Science, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Longfei Chen
- International Innovation Institute, Beihang University, Hangzhou 311115, China
- School of Energy and Power Engineering, Beihang University, Beijing 100191, China
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11
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Montero de Hijes P, Dellago C, Jinnouchi R, Schmiedmayer B, Kresse G. Comparing machine learning potentials for water: Kernel-based regression and Behler-Parrinello neural networks. J Chem Phys 2024; 160:114107. [PMID: 38506284 DOI: 10.1063/5.0197105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 03/03/2024] [Indexed: 03/21/2024] Open
Abstract
In this paper, we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE + D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks trained on a highly accurate dataset consisting of about 1500 structures, as well as a smaller dataset, about half the size, obtained using only on-the-fly learning. This study reveals that despite minor differences between the MLPs, their agreement on observables such as the diffusion constant and pair-correlation functions is excellent, especially for the large training dataset. Variations in the predicted density isobars, albeit somewhat larger, are also acceptable, particularly given the errors inherent to approximate density functional theory. Overall, this study emphasizes the relevance of the database over the fitting method. Finally, this study underscores the limitations of root mean square errors and the need for comprehensive testing, advocating the use of multiple MLPs for enhanced certainty, particularly when simulating complex thermodynamic properties that may not be fully captured by simpler tests.
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Affiliation(s)
- Pablo Montero de Hijes
- University of Vienna, Faculty of Physics, Kolingasse 14, A-1090 Vienna, Austria
- University of Vienna, Faculty of Earth Sciences, Geography and Astronomy, Josef-Holaubuek-Platz 2, 1090 Vienna, Austria
| | - Christoph Dellago
- University of Vienna, Faculty of Physics, Kolingasse 14, A-1090 Vienna, Austria
| | - Ryosuke Jinnouchi
- Toyota Central R&D Labs., Inc., 41-1 Yokomichi, Nagakute, Aichi 480-1192, Japan
| | | | - Georg Kresse
- University of Vienna, Faculty of Physics, Kolingasse 14, A-1090 Vienna, Austria
- VASP Software GmbH, Berggasse 21, A-1090 Vienna, Austria
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12
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Song Z, Han J, Henkelman G, Li L. Charge-Optimized Electrostatic Interaction Atom-Centered Neural Network Algorithm. J Chem Theory Comput 2024; 20:2088-2097. [PMID: 38380601 DOI: 10.1021/acs.jctc.3c01254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Machine-learning algorithms have been proposed to capture electrostatic interactions by using effective partial charges. These algorithms often rely on a pretrained model for partial charge prediction using density functional theory-calculated partial charges as references, which introduces complexity to the force field model. The accuracy of the trained model also depends on the reliability of charge partition methods, which can be dependent on the specific system and methodology employed. In this study, we propose an atom-centered neural network (ANN) algorithm that eliminates the need for reference charges. Our algorithm requires only a single NN model for each element to obtain both atomic energy and charges. These atomic charges are then employed to compute electrostatic energies using the Ewald summation algorithm. Subsequently, the force field model is trained on total energy and forces, with the inclusion of electrostatic energy. To evaluate the performance of our algorithm, we conducted tests on three benchmark systems, including a Ge slab with an O adatom system, a TiO2 crystalline system, and a Pd-O nanoparticle system. Our results demonstrate reasonably accurate predictions of partial charges and electrostatic interactions. This algorithm provides a self-consistent charge prediction strategy and possibilities for robust and reliable modeling of electrostatic interactions in machine-learning potentials.
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Affiliation(s)
- Zichen Song
- Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM), Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Jian Han
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Graeme Henkelman
- Department of Chemistry, the University of Texas at Austin, Austin, Texas 78712, United States
- Institute for Computational Engineering and Sciences, the University of Texas at Austin, Austin, Texas 78712, United States
| | - Lei Li
- Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM), Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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13
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Matsumoto M, Yagasaki T, Tanaka H. GenIce-core: Efficient algorithm for generation of hydrogen-disordered ice structures. J Chem Phys 2024; 160:094101. [PMID: 38426513 DOI: 10.1063/5.0198056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/09/2024] [Indexed: 03/02/2024] Open
Abstract
Ice is different from ordinary crystals because it contains randomness, which means that statistical treatment based on ensemble averaging is essential. Ice structures are constrained by topological rules known as the ice rules, which give them unique anomalous properties. These properties become more apparent when the system size is large. For this reason, there is a need to produce a large number of sufficiently large crystals that are homogeneously random and satisfy the ice rules. We have developed an algorithm to quickly generate ice structures containing ions and defects. This algorithm is provided as an independent software module that can be incorporated into crystal structure generation software. By doing so, it becomes possible to simulate ice crystals on a previously impossible scale.
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Affiliation(s)
- Masakazu Matsumoto
- Research Institute for Interdisciplinary Science, Okayama University, Okayama 700-8530, Japan
| | - Takuma Yagasaki
- Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan
| | - Hideki Tanaka
- Research Institute for Interdisciplinary Science, Okayama University, Okayama 700-8530, Japan
- Toyota Physical and Chemical Research Institute, Nagakute 480-1192, Japan
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14
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Walsh MR. Comparing brute force to transition path sampling for gas hydrate nucleation with a flat interface: comments on time reversal symmetry. Phys Chem Chem Phys 2024; 26:5762-5772. [PMID: 38214888 DOI: 10.1039/d3cp05059a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Fluid to solid nucleation is often investigated with the rare event method transition path sampling (TPS). I claim that the inherent irreversibility of solid nucleation, even at stationary conditions, calls into question TPS's applicability for determining solid nucleation mechanisms, especially for pre-critical behavior. Even when applied to a phenomenon which displays time reversal asymmetry like solid nucleation, TPS is a good means of exploring phase space and giving trends in post-critical structure, and its ability to facilitate nucleation rate and free energy calculations remains outstanding. Forward-only splitting and ratcheting methods such as forward flux sampling are more attractive for understanding nucleation mechanisms as they do not require time reversal symmetry, but at low driving forces may suffer from the same limitations as brute force: they may never make it to the first ratchet. Here I briefly summarize the TPS method and gas hydrate nucleation simulation literature, focusing on topics within both to facilitate a comparison of brute force hydrate nucleation to transition path sampling of hydrate nucleation. Perhaps anecdotally, the brute force technique results in more crystalline trajectories despite having higher driving forces than TPS. I maintain this difference is because of the inherent irreversibility of hydrate nucleation, meaning its pre-critical behavior cannot accurately be determined by the melting trajectories that comprise approximately half of the configurations in TPS's path ensemble.
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15
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Gromoff Q, Benzo P, Saidi WA, Andolina CM, Casanove MJ, Hungria T, Barre S, Benoit M, Lam J. Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations. NANOSCALE 2023; 16:384-393. [PMID: 38063839 DOI: 10.1039/d3nr04471h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
While nanoalloys are of paramount scientific and practical interest, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including the crystal phase, chemical ordering, and morphology, are challenging to control at the nanoscale, making it difficult to extend their use to industrial applications. In this contribution, we focus on the gold/silver system that has two of the most prevalent noble metals and combine experiments with simulations to uncover the formation mechanisms at the atomic level. Nanoparticles were produced using a state-of-the-art inert-gas aggregation source and analyzed using transmission electron microscopy and energy-dispersive X-ray spectroscopy. Machine-learning-assisted molecular dynamics simulations were employed to model the crystallization process from liquid droplets to nanocrystals. Our study finds a preponderance of nanoparticles with five-fold symmetric morphology, including icosahedra and decahedra which is consistent with previous results on mono-metallic nanoparticles. However, we observed that gold atoms, rather than silver atoms, segregate at the surface of the obtained nanoparticles for all the considered alloy compositions. These segregation tendencies are in contrast to previous studies and have consequences on the crystallization dynamics and the subsequent crystal ordering. We finally showed that the underpinning of this surprising segregation dynamics is due to charge transfer and electrostatic interactions rather than surface energy considerations.
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Affiliation(s)
- Quentin Gromoff
- CEMES, CNRS and Université de Toulouse, 29 rue Jeanne Marvig, 31055 Toulouse Cedex, France
| | - Patrizio Benzo
- CEMES, CNRS and Université de Toulouse, 29 rue Jeanne Marvig, 31055 Toulouse Cedex, France
| | - Wissam A Saidi
- National Energy Technology Laboratory, United States Department of Energy, Pittsburgh, PA 15236, USA
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Christopher M Andolina
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Marie-José Casanove
- CEMES, CNRS and Université de Toulouse, 29 rue Jeanne Marvig, 31055 Toulouse Cedex, France
| | - Teresa Hungria
- Centre de MicroCaractérisation Raimond Castaing, Université de Toulouse, 3 rue Caroline Aigle, F-31400 Toulouse, France
| | - Sophie Barre
- CEMES, CNRS and Université de Toulouse, 29 rue Jeanne Marvig, 31055 Toulouse Cedex, France
| | - Magali Benoit
- CEMES, CNRS and Université de Toulouse, 29 rue Jeanne Marvig, 31055 Toulouse Cedex, France
| | - Julien Lam
- CEMES, CNRS and Université de Toulouse, 29 rue Jeanne Marvig, 31055 Toulouse Cedex, France
- Univ. Lille, CNRS, INRA, ENSCL, UMR 8207, UMET, Unité Matériaux et Transformations, F 59000 Lille, France.
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16
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Wu S, Yang X, Zhao X, Li Z, Lu M, Xie X, Yan J. Applications and Advances in Machine Learning Force Fields. J Chem Inf Model 2023; 63:6972-6985. [PMID: 37751546 DOI: 10.1021/acs.jcim.3c00889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Force fields (FFs) form the basis of molecular simulations and have significant implications in diverse fields such as materials science, chemistry, physics, and biology. A suitable FF is required to accurately describe system properties. However, an off-the-shelf FF may not be suitable for certain specialized systems, and researchers often need to tailor the FF that fits specific requirements. Before applying machine learning (ML) techniques to construct FFs, the mainstream FFs were primarily based on first-principles force fields (FPFF) and empirical FFs. However, the drawbacks of FPFF and empirical FFs are high cost and low accuracy, respectively, so there is a growing interest in using ML as an effective and precise tool for reconciling this trade-off in developing FFs. In this review, we introduce the fundamental principles of ML and FFs in the context of machine learning force fields (MLFF). We also discuss the advantages and applications of MLFF compared to traditional FFs, as well as the MLFF toolkits widely employed in numerous applications.
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Affiliation(s)
- Shiru Wu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Xiaowei Yang
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Xun Zhao
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Zhipu Li
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Min Lu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Xiaoji Xie
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Jiaxu Yan
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
- Changchun Institute of Optics, Fine Mechanics & Physics (CIOMP), Chinese Academy of Sciences, Changchun 130033, P. R. China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, P. R. China
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17
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Palos E, Caruso A, Paesani F. Consistent density functional theory-based description of ion hydration through density-corrected many-body representations. J Chem Phys 2023; 159:181101. [PMID: 37947509 DOI: 10.1063/5.0174577] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023] Open
Abstract
Delocalization error constrains the accuracy of density functional theory in describing molecular interactions in ion-water systems. Using Na+ and Cl- in water as model systems, we calculate the effects of delocalization error in the SCAN functional for describing ion-water and water-water interactions in hydrated ions, and demonstrate that density-corrected SCAN (DC-SCAN) predicts n-body and interaction energies with an accuracy approaching coupled cluster theory. The performance of DC-SCAN is size-consistent, maintaining an accurate description of molecular interactions well beyond the first solvation shell. Molecular dynamics simulations at ambient conditions with many-body MB-SCAN(DC) potentials, derived from the many-body expansion, predict the solvation structure of Na+ and Cl- in quantitative agreement with reference data, while simultaneously reproducing the structure of liquid water. Beyond rationalizing the accuracy of density-corrected models of ion hydration, our findings suggest that our unified density-corrected MB formalism holds great promise for efficient DFT-based simulations of condensed-phase systems with chemical accuracy.
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Affiliation(s)
- Etienne Palos
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Alessandro Caruso
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
- Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, USA
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, USA
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18
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Shi J, Liang Z, Wang J, Pan S, Ding C, Wang Y, Wang HT, Xing D, Sun J. Double-Shock Compression Pathways from Diamond to BC8 Carbon. PHYSICAL REVIEW LETTERS 2023; 131:146101. [PMID: 37862650 DOI: 10.1103/physrevlett.131.146101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/11/2023] [Accepted: 09/08/2023] [Indexed: 10/22/2023]
Abstract
Carbon is one of the most important elements for both industrial applications and fundamental research, including life, physics, chemistry, materials, and even planetary science. Although theoretical predictions on the transition from diamond to the BC8 (Ia3[over ¯]) carbon were made more than thirty years ago, after tremendous experimental efforts, direct evidence for the existence of BC8 carbon is still lacking. In this study, a machine learning potential was developed for high-pressure carbon fitted from first-principles calculations, which exhibited great capabilities in modeling the melting and Hugoniot line. Using the molecular dynamics based on this machine learning potential, we designed a thermodynamic pathway that is achievable for the double shock compression experiment to obtain the elusive BC8 carbon. Diamond was compressed up to 584 GPa after the first shock at 20.5 km/s. Subsequently, in the second shock compression at 24.8 or 25.0 km/s, diamond was compressed to a supercooled liquid and then solidified to BC8 in around 1 ns. Furthermore, the critical nucleus size and nucleation rate of BC8 were calculated, which are crucial for nano-second x-ray diffraction measurements to observe BC8 carbon during shock compressions. The key to obtaining BC8 carbon lies in the formation of liquid at a sufficient supercooling. Our work provides a feasible pathway by which the long-sought BC8 phase of carbon can be reached in experiments.
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Affiliation(s)
- Jiuyang Shi
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, People's Republic of China
| | - Zhixing Liang
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, People's Republic of China
| | - Junjie Wang
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, People's Republic of China
| | - Shuning Pan
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, People's Republic of China
| | - Chi Ding
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, People's Republic of China
| | - Yong Wang
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, People's Republic of China
| | - Hui-Tian Wang
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, People's Republic of China
| | - Dingyu Xing
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, People's Republic of China
| | - Jian Sun
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, People's Republic of China
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19
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Palmer JC, Sarupria S, Truskett TM. Tribute to Pablo G. Debenedetti. J Phys Chem B 2023; 127:8075-8078. [PMID: 37766640 DOI: 10.1021/acs.jpcb.3c06020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Affiliation(s)
- Jeremy C Palmer
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
| | - Sapna Sarupria
- Department of Chemistry, Chemical Theory Center, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering and Department of Physics, The University of Texas at Austin, Austin, Texas 78712, United States
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20
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Blazquez S, Abascal JLF, Lagerweij J, Habibi P, Dey P, Vlugt TJH, Moultos OA, Vega C. Computation of Electrical Conductivities of Aqueous Electrolyte Solutions: Two Surfaces, One Property. J Chem Theory Comput 2023; 19:5380-5393. [PMID: 37506381 PMCID: PMC10448725 DOI: 10.1021/acs.jctc.3c00562] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Indexed: 07/30/2023]
Abstract
In this work, we computed electrical conductivities under ambient conditions of aqueous NaCl and KCl solutions by using the Einstein-Helfand equation. Common force fields (charge q = ±1 e) do not reproduce the experimental values of electrical conductivities, viscosities, and diffusion coefficients. Recently, we proposed the idea of using different charges to describe the potential energy surface (PES) and the dipole moment surface (DMS). In this work, we implement this concept. The equilibrium trajectories required to evaluate electrical conductivities (within linear response theory) were obtained by using scaled charges (with the value q = ±0.75 e) to describe the PES. The potential parameters were those of the Madrid-Transport force field, which accurately describe viscosities and diffusion coefficients of these ionic solutions. However, integer charges were used to compute the conductivities (thus describing the DMS). The basic idea is that although the scaled charge describes the ion-water interaction better, the integer charge reflects the value of the charge that is transported due to the electric field. The agreement obtained with experiments is excellent, as for the first time electrical conductivities (and the other transport properties) of NaCl and KCl electrolyte solutions are described with high accuracy for the whole concentration range up to their solubility limit. Finally, we propose an easy way to obtain a rough estimate of the actual electrical conductivity of the potential model under consideration using the approximate Nernst-Einstein equation, which neglects correlations between different ions.
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Affiliation(s)
- Samuel Blazquez
- Dpto.
Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Jose L. F. Abascal
- Dpto.
Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Jelle Lagerweij
- Engineering
Thermodynamics, Process and Energy Department, Faculty of Mechanical,
Maritime and Materials Engineering, Delft
University of Technology, Leeghwaterstraat 39, 2628CB Delft, The Netherlands
| | - Parsa Habibi
- Engineering
Thermodynamics, Process and Energy Department, Faculty of Mechanical,
Maritime and Materials Engineering, Delft
University of Technology, Leeghwaterstraat 39, 2628CB Delft, The Netherlands
- Department
of Materials Science and Engineering, Faculty of Mechanical, Maritime
and Materials Engineering, Delft University
of Technology, Mekelweg
2, 2628CD Delft, The Netherlands
| | - Poulumi Dey
- Department
of Materials Science and Engineering, Faculty of Mechanical, Maritime
and Materials Engineering, Delft University
of Technology, Mekelweg
2, 2628CD Delft, The Netherlands
| | - Thijs J. H. Vlugt
- Engineering
Thermodynamics, Process and Energy Department, Faculty of Mechanical,
Maritime and Materials Engineering, Delft
University of Technology, Leeghwaterstraat 39, 2628CB Delft, The Netherlands
| | - Othonas A. Moultos
- Engineering
Thermodynamics, Process and Energy Department, Faculty of Mechanical,
Maritime and Materials Engineering, Delft
University of Technology, Leeghwaterstraat 39, 2628CB Delft, The Netherlands
| | - Carlos Vega
- Dpto.
Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
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21
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Zeng J, Zhang D, Lu D, Mo P, Li Z, Chen Y, Rynik M, Huang L, Li Z, Shi S, Wang Y, Ye H, Tuo P, Yang J, Ding Y, Li Y, Tisi D, Zeng Q, Bao H, Xia Y, Huang J, Muraoka K, Wang Y, Chang J, Yuan F, Bore SL, Cai C, Lin Y, Wang B, Xu J, Zhu JX, Luo C, Zhang Y, Goodall REA, Liang W, Singh AK, Yao S, Zhang J, Wentzcovitch R, Han J, Liu J, Jia W, York DM, E W, Car R, Zhang L, Wang H. DeePMD-kit v2: A software package for deep potential models. J Chem Phys 2023; 159:054801. [PMID: 37526163 PMCID: PMC10445636 DOI: 10.1063/5.0155600] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/03/2023] [Indexed: 08/02/2023] Open
Abstract
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.
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Affiliation(s)
- Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | | | - Denghui Lu
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, People’s Republic of China
| | - Pinghui Mo
- College of Electrical and Information Engineering, Hunan University, Changsha, People’s Republic of China
| | - Zeyu Li
- Yuanpei College, Peking University, Beijing 100871, People’s Republic of China
| | - Yixiao Chen
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08540, USA
| | - Marián Rynik
- Department of Experimental Physics, Comenius University, Mlynská Dolina F2, 842 48 Bratislava, Slovakia
| | - Li’ang Huang
- Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
| | | | - Shaochen Shi
- ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
| | | | - Haotian Ye
- Yuanpei College, Peking University, Beijing 100871, People’s Republic of China
| | - Ping Tuo
- AI for Science Institute, Beijing 100080, People’s Republic of China
| | - Jiabin Yang
- Baidu, Inc., Beijing, People’s Republic of China
| | | | - Yifan Li
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | | | - Qiyu Zeng
- Department of Physics, National University of Defense Technology, Changsha, Hunan 410073, People’s Republic of China
| | | | - Yu Xia
- ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
| | | | - Koki Muraoka
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Yibo Wang
- DP Technology, Beijing 100080, People’s Republic of China
| | | | - Fengbo Yuan
- DP Technology, Beijing 100080, People’s Republic of China
| | - Sigbjørn Løland Bore
- Hylleraas Centre for Quantum Molecular Sciences and Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, 0315 Oslo, Norway
| | | | - Yinnian Lin
- Wangxuan Institute of Computer Technology, Peking University, Beijing 100871, People’s Republic of China
| | - Bo Wang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry and Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, People’s Republic of China
| | - Jiayan Xu
- School of Chemistry and Chemical Engineering, Queen’s University Belfast, Belfast BT9 5AG, United Kingdom
| | - Jia-Xin Zhu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People’s Republic of China
| | - Chenxing Luo
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
| | - Yuzhi Zhang
- DP Technology, Beijing 100080, People’s Republic of China
| | | | - Wenshuo Liang
- DP Technology, Beijing 100080, People’s Republic of China
| | - Anurag Kumar Singh
- Department of Data Science, Indian Institute of Technology, Palakkad, Kerala, India
| | - Sikai Yao
- DP Technology, Beijing 100080, People’s Republic of China
| | - Jingchao Zhang
- NVIDIA AI Technology Center (NVAITC), Santa Clara, California 95051, USA
| | | | - Jiequn Han
- Center for Computational Mathematics, Flatiron Institute, New York, New York 10010, USA
| | - Jie Liu
- College of Electrical and Information Engineering, Hunan University, Changsha, People’s Republic of China
| | | | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | | | - Roberto Car
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | | | - Han Wang
- Author to whom correspondence should be addressed:
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22
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Piaggi PM, Gartner TE, Car R, Debenedetti PG. Melting curves of ice polymorphs in the vicinity of the liquid-liquid critical point. J Chem Phys 2023; 159:054502. [PMID: 37531247 DOI: 10.1063/5.0159288] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 07/14/2023] [Indexed: 08/04/2023] Open
Abstract
The possible existence of a liquid-liquid critical point in deeply supercooled water has been a subject of debate due to the challenges associated with providing definitive experimental evidence. The pioneering work by Mishima and Stanley [Nature 392, 164-168 (1998)] sought to shed light on this problem by studying the melting curves of different ice polymorphs and their metastable continuation in the vicinity of the expected liquid-liquid transition and its associated critical point. Based on the continuous or discontinuous changes in the slope of the melting curves, Mishima [Phys. Rev. Lett. 85, 334 (2000)] suggested that the liquid-liquid critical point lies between the melting curves of ice III and ice V. We explore this conjecture using molecular dynamics simulations with a machine learning model based on ab initio quantum-mechanical calculations. We study the melting curves of ices III, IV, V, VI, and XIII and find that all of them are supercritical and do not intersect the liquid-liquid transition locus. We also find a pronounced, yet continuous, change in the slope of the melting lines upon crossing of the liquid locus of maximum compressibility. Finally, we analyze the literature in light of our findings and conclude that the scenario in which the melting curves are supercritical is favored by the most recent computational and experimental evidence. Although the preponderance of evidence is consistent with the existence of a second critical point in water, the behavior of ice polymorph melting lines does not provide strong evidence in support of this viewpoint, according to our calculations.
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Affiliation(s)
- Pablo M Piaggi
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | - Thomas E Gartner
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
| | - Roberto Car
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
- Department of Physics, Princeton University, Princeton, New Jersey 08544, USA
| | - Pablo G Debenedetti
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
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23
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Lin B, Jiang J, Zeng XC, Li L. Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field. Nat Commun 2023; 14:4110. [PMID: 37433823 DOI: 10.1038/s41467-023-39829-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 06/23/2023] [Indexed: 07/13/2023] Open
Abstract
Understanding the phase behaviour of nanoconfined water films is of fundamental importance in broad fields of science and engineering. However, the phase behaviour of the thinnest water film - monolayer water - is still incompletely known. Here, we developed a machine-learning force field (MLFF) at first-principles accuracy to determine the phase diagram of monolayer water/ice in nanoconfinement with hydrophobic walls. We observed the spontaneous formation of two previously unreported high-density ices, namely, zigzag quasi-bilayer ice (ZZ-qBI) and branched-zigzag quasi-bilayer ice (bZZ-qBI). Unlike conventional bilayer ices, few inter-layer hydrogen bonds were observed in both quasi-bilayer ices. Notably, the bZZ-qBI entails a unique hydrogen-bonding network that consists of two distinctive types of hydrogen bonds. Moreover, we identified, for the first time, the stable region for the lowest-density [Formula: see text] monolayer ice (LD-48MI) at negative pressures (<-0.3 GPa). Overall, the MLFF enables large-scale first-principle-level molecular dynamics (MD) simulations of the spontaneous transition from the liquid water to a plethora of monolayer ices, including hexagonal, pentagonal, square, zigzag (ZZMI), and hexatic monolayer ices. These findings will enrich our understanding of the phase behaviour of the nanoconfined water/ices and provide a guide for future experimental realization of the 2D ices.
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Affiliation(s)
- Bo Lin
- Guangdong Provincial Key Laboratory of Functional Oxide Materials and Devices, Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China
| | - Jian Jiang
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Xiao Cheng Zeng
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong.
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.
| | - Lei Li
- Guangdong Provincial Key Laboratory of Functional Oxide Materials and Devices, Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China.
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24
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Rogal J, Díaz Leines G. Controlling crystallization: what liquid structure and dynamics reveal about crystal nucleation mechanisms. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220249. [PMID: 37211029 DOI: 10.1098/rsta.2022.0249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 12/06/2022] [Indexed: 05/23/2023]
Abstract
Over recent years, molecular simulations have provided invaluable insights into the microscopic processes governing the initial stages of crystal nucleation and growth. A key aspect that has been observed in many different systems is the formation of precursors in the supercooled liquid that precedes the emergence of crystalline nuclei. The structural and dynamical properties of these precursors determine to a large extent the nucleation probability as well as the formation of specific polymorphs. This novel microscopic view on nucleation mechanisms has further implications for our understanding of the nucleating ability and polymorph selectivity of nucleating agents, as these appear to be strongly linked to their ability in modifying structural and dynamical characteristics of the supercooled liquid, namely liquid heterogeneity. In this perspective, we highlight recent progress in exploring the connection between liquid heterogeneity and crystallization, including the effects of templates, and the potential impact for controlling crystallization processes. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.
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Affiliation(s)
- Jutta Rogal
- Department of Chemistry, New York University, New York, NY 10003, USA
- Fachbereich Physik, Freie Universität Berlin, 14195 Berlin, Germany
| | - Grisell Díaz Leines
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
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25
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Bore SL, Paesani F. Realistic phase diagram of water from "first principles" data-driven quantum simulations. Nat Commun 2023; 14:3349. [PMID: 37291095 PMCID: PMC10250386 DOI: 10.1038/s41467-023-38855-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/12/2023] [Indexed: 06/10/2023] Open
Abstract
Since the experimental characterization of the low-pressure region of water's phase diagram in the early 1900s, scientists have been on a quest to understand the thermodynamic stability of ice polymorphs on the molecular level. In this study, we demonstrate that combining the MB-pol data-driven many-body potential for water, which was rigorously derived from "first principles" and exhibits chemical accuracy, with advanced enhanced-sampling algorithms, which correctly describe the quantum nature of molecular motion and thermodynamic equilibria, enables computer simulations of water's phase diagram with an unprecedented level of realism. Besides providing fundamental insights into how enthalpic, entropic, and nuclear quantum effects shape the free-energy landscape of water, we demonstrate that recent progress in "first principles" data-driven simulations, which rigorously encode many-body molecular interactions, has opened the door to realistic computational studies of complex molecular systems, bridging the gap between experiments and simulations.
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Affiliation(s)
- Sigbjørn Løland Bore
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093, USA.
- Materials Science and Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
- Halicioğlu Data Science Institute, University of California San Diego, La Jolla, CA, 92093, USA.
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, 92093, USA.
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26
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Sanchez-Burgos I, Muniz MC, Espinosa JR, Panagiotopoulos AZ. A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water. J Chem Phys 2023; 158:2889532. [PMID: 37158636 DOI: 10.1063/5.0144500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/30/2023] [Indexed: 05/10/2023] Open
Abstract
Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here, we utilize the Deep Potential methodology-a machine learning approach-to study this ubiquitous phase transition, starting from the phase diagram in the liquid-vapor coexistence regime. The machine learning model is trained on ab initio energies and forces based on the SCAN density functional, which has been previously shown to reproduce solid phases and other properties of water. Here, we compute the surface tension, saturation pressure, and enthalpy of vaporization for a range of temperatures spanning from 300 to 600 K and evaluate the Deep Potential model performance against experimental results and the semiempirical TIP4P/2005 classical model. Moreover, by employing the seeding technique, we evaluate the free energy barrier and nucleation rate at negative pressures for the isotherm of 296.4 K. We find that the nucleation rates obtained from the Deep Potential model deviate from those computed for the TIP4P/2005 water model due to an underestimation in the surface tension from the Deep Potential model. From analysis of the seeding simulations, we also evaluate the Tolman length for the Deep Potential water model, which is (0.091 ± 0.008) nm at 296.4 K. Finally, we identify that water molecules display a preferential orientation in the liquid-vapor interface, in which H atoms tend to point toward the vapor phase to maximize the enthalpic gain of interfacial molecules. We find that this behavior is more pronounced for planar interfaces than for the curved interfaces in bubbles. This work represents the first application of Deep Potential models to the study of liquid-vapor coexistence and water cavitation.
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Affiliation(s)
- Ignacio Sanchez-Burgos
- Maxwell Centre, Cavendish Laboratory, Department of Physics, University of Cambridge, J J Thomson Avenue,Cambridge CB3 0HE, United Kingdom
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Maria Carolina Muniz
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Jorge R Espinosa
- Maxwell Centre, Cavendish Laboratory, Department of Physics, University of Cambridge, J J Thomson Avenue,Cambridge CB3 0HE, United Kingdom
- Departamento de Química Fisica, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
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27
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Guidarelli Mattioli F, Sciortino F, Russo J. Are Neural Network Potentials Trained on Liquid States Transferable to Crystal Nucleation? A Test on Ice Nucleation in the mW Water Model. J Phys Chem B 2023; 127:3894-3901. [PMID: 37075256 PMCID: PMC10165654 DOI: 10.1021/acs.jpcb.3c00693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/06/2023] [Indexed: 04/21/2023]
Abstract
Neural network potentials (NNPs) are increasingly being used to study processes that happen on long time scales. A typical example is crystal nucleation, which rate is controlled by the occurrence of a rare fluctuation, i.e., the appearance of the critical nucleus. Because the properties of this nucleus are far from those of the bulk crystal, it is yet unclear whether NN potentials trained on equilibrium liquid states can accurately describe nucleation processes. So far, nucleation studies on NNPs have been limited to ab initio models whose nucleation properties are unknown, preventing an accurate comparison. Here we train a NN potential on the mW model of water─a classical three-body potential whose nucleation time scale is accessible in standard simulations. We show that a NNP trained only on a small number of liquid state points can reproduce with great accuracy the nucleation rates and free energy barriers of the original model, computed from both spontaneous and biased trajectories, strongly supporting the use of NNPs to study nucleation events.
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Affiliation(s)
| | | | - John Russo
- Sapienza University of Rome, Piazzale Aldo Moro 2, 00185 Rome, Italy
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28
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Montero de Hijes P, R Espinosa J, Vega C, Dellago C. Minimum in the pressure dependence of the interfacial free energy between ice Ih and water. J Chem Phys 2023; 158:124503. [PMID: 37003785 DOI: 10.1063/5.0140814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
Abstract
Despite the importance of ice nucleation, this process has been barely explored at negative pressures. Here, we study homogeneous ice nucleation in stretched water by means of molecular dynamics seeding simulations using the TIP4P/Ice model. We observe that the critical nucleus size, interfacial free energy, free energy barrier, and nucleation rate barely change between isobars from -2600 to 500 bars when they are represented as a function of supercooling. This allows us to identify universal empirical expressions for homogeneous ice nucleation in the pressure range from -2600 to 500 bars. We show that this universal behavior arises from the pressure dependence of the interfacial free energy, which we compute by means of the mold integration technique, finding a shallow minimum around -2000 bars. Likewise, we show that the change in the interfacial free energy with pressure is proportional to the excess entropy and the slope of the melting line, exhibiting in the latter a reentrant behavior also at the same negative pressure. Finally, we estimate the excess internal energy and the excess entropy of the ice Ih-water interface.
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Affiliation(s)
| | - J R Espinosa
- Departamento de Química Física, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - C Vega
- Departamento de Química Física, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - C Dellago
- Faculty of Physics, University of Vienna, A-1090 Vienna, Austria
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29
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Grabowska J, Blazquez S, Sanz E, Noya EG, Zeron IM, Algaba J, Miguez JM, Blas FJ, Vega C. Homogeneous nucleation rate of methane hydrate formation under experimental conditions from seeding simulations. J Chem Phys 2023; 158:114505. [PMID: 36948790 DOI: 10.1063/5.0132681] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
Abstract
In this work, we shall estimate via computer simulations the homogeneous nucleation rate for the methane hydrate at 400 bars for a supercooling of about 35 K. The TIP4P/ICE model and a Lennard-Jones center were used for water and methane, respectively. To estimate the nucleation rate, the seeding technique was employed. Clusters of the methane hydrate of different sizes were inserted into the aqueous phase of a two-phase gas-liquid equilibrium system at 260 K and 400 bars. Using these systems, we determined the size at which the cluster of the hydrate is critical (i.e., it has 50% probability of either growing or melting). Since nucleation rates estimated from the seeding technique are sensitive to the choice of the order parameter used to determine the size of the cluster of the solid, we considered several possibilities. We performed brute force simulations of an aqueous solution of methane in water in which the concentration of methane was several times higher than the equilibrium concentration (i.e., the solution was supersaturated). From brute force runs, we infer the value of the nucleation rate for this system rigorously. Subsequently, seeding runs were carried out for this system, and it was found that only two of the considered order parameters were able to reproduce the value of the nucleation rate obtained from brute force simulations. By using these two order parameters, we estimated the nucleation rate under experimental conditions (400 bars and 260 K) to be of the order of log10 (J/(m3 s)) = -7(5).
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Affiliation(s)
- J Grabowska
- Dpto. Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - S Blazquez
- Dpto. Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - E Sanz
- Dpto. Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - E G Noya
- Instituto de Química Física Rocasolano, CSIC, C/ Serrano 119, 28006 Madrid, Spain
| | - I M Zeron
- Laboratorio de Simulación Molecular y Química Computacional, CIQSO-Centro de Investigación en Química Sostenible and Departamento de Ciencias Integradas, Universidad de Huelva, 21006 Huelva, Spain
| | - J Algaba
- Laboratorio de Simulación Molecular y Química Computacional, CIQSO-Centro de Investigación en Química Sostenible and Departamento de Ciencias Integradas, Universidad de Huelva, 21006 Huelva, Spain
| | - J M Miguez
- Laboratorio de Simulación Molecular y Química Computacional, CIQSO-Centro de Investigación en Química Sostenible and Departamento de Ciencias Integradas, Universidad de Huelva, 21006 Huelva, Spain
| | - F J Blas
- Laboratorio de Simulación Molecular y Química Computacional, CIQSO-Centro de Investigación en Química Sostenible and Departamento de Ciencias Integradas, Universidad de Huelva, 21006 Huelva, Spain
| | - C Vega
- Dpto. Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
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30
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Zou Z, Beyerle ER, Tsai ST, Tiwary P. Driving and characterizing nucleation of urea and glycine polymorphs in water. Proc Natl Acad Sci U S A 2023; 120:e2216099120. [PMID: 36757888 PMCID: PMC9963467 DOI: 10.1073/pnas.2216099120] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 01/17/2023] [Indexed: 02/10/2023] Open
Abstract
Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here, we employ the machine learning-augmented molecular dynamics framework "reweighted autoencoded variational Bayes for enhanced sampling (RAVE)." We study two molecular systems-urea and glycine-in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth nucleation events of different polymorphs from homogeneous solution; from these trajectories, we calculate the relative ranking of finite-sized polymorph crystals embedded in solution, in terms of the free-energy difference between the finite-sized crystal polymorph and the original solution state. We further observe that the obtained reaction coordinates and transitions are highly nonclassical.
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Affiliation(s)
- Ziyue Zou
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD20742
| | - Eric R. Beyerle
- Institute for Physical Science and Technology, University of Maryland, College Park, MD20742
| | - Sun-Ting Tsai
- Department of Physics, University of Maryland, College Park, MD20742
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD20742
- Institute for Physical Science and Technology, University of Maryland, College Park, MD20742
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31
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Kee CW. Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis-Challenges and Opportunities. Molecules 2023; 28:1715. [PMID: 36838703 PMCID: PMC9966076 DOI: 10.3390/molecules28041715] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/25/2023] Open
Abstract
Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.
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Affiliation(s)
- Choon Wee Kee
- Institute of Sustainability for Chemicals, Energy and Environment (ISCE2), Agency for Science, Technology and Research (A*STAR), 1 Pesek Road, Jurong Island, Singapore 627833, Republic of Singapore
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32
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Blazquez S, Conde MM, Vega C. Scaled charges for ions: An improvement but not the final word for modeling electrolytes in water. J Chem Phys 2023; 158:054505. [PMID: 36754806 DOI: 10.1063/5.0136498] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
In this work, we discuss the use of scaled charges when developing force fields for NaCl in water. We shall develop force fields for Na+ and Cl- using the following values for the scaled charge (in electron units): ±0.75, ±0.80, ±0.85, and ±0.92 along with the TIP4P/2005 model of water (for which previous force fields were proposed for q = ±0.85 and q = ±1). The properties considered in this work are densities, structural properties, transport properties, surface tension, freezing point depression, and maximum in density. All the developed models were able to describe quite well the experimental values of the densities. Structural properties were well described by models with charges equal to or larger than ±0.85, surface tension by the charge ±0.92, maximum in density by the charge ±0.85, and transport properties by the charge ±0.75. The use of a scaled charge of ±0.75 is able to reproduce with high accuracy the viscosities and diffusion coefficients of NaCl solutions for the first time. We have also considered the case of KCl in water, and the results obtained were fully consistent with those of NaCl. There is no value of the scaled charge able to reproduce all the properties considered in this work. Although certainly scaled charges are not the final word in the development of force fields for electrolytes in water, its use may have some practical advantages. Certain values of the scaled charge could be the best option when the interest is to describe certain experimental properties.
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Affiliation(s)
- S Blazquez
- Dpto. Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - M M Conde
- Departamento de Ingeniería Química Industrial y Medio Ambiente, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain
| | - C Vega
- Dpto. Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
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33
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Panagiotopoulos AZ, Yue S. Dynamics of Aqueous Electrolyte Solutions: Challenges for Simulations. J Phys Chem B 2023; 127:430-437. [PMID: 36607836 DOI: 10.1021/acs.jpcb.2c07477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
This Perspective article focuses on recent simulation work on the dynamics of aqueous electrolytes. It is well-established that full-charge, nonpolarizable models for water and ions generally predict solution dynamics that are too slow in comparison to experiments. Models with reduced (scaled) charges do better for solution diffusivities and viscosities but encounter issues describing other dynamic phenomena such as nucleation rates of crystals from solution. Polarizable models show promise, especially when appropriately parametrized, but may still miss important physical effects such as charge transfer. First-principles calculations are starting to emerge for these properties that are in principle able to capture polarization, charge transfer, and chemical transformations in solution. While direct ab initio simulations are still too slow for simulations of large systems over long time scales, machine-learning models trained on appropriate first-principles data show significant promise for accurate and transferable modeling of electrolyte solution dynamics.
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Affiliation(s)
| | - Shuwen Yue
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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34
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Gartner TE, Piaggi PM, Car R, Panagiotopoulos AZ, Debenedetti PG. Liquid-Liquid Transition in Water from First Principles. PHYSICAL REVIEW LETTERS 2022; 129:255702. [PMID: 36608224 DOI: 10.1103/physrevlett.129.255702] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 11/14/2022] [Indexed: 06/17/2023]
Abstract
A long-standing question in water research is the possibility that supercooled liquid water can undergo a liquid-liquid phase transition (LLT) into high- and low-density liquids. We used several complementary molecular simulation techniques to evaluate the possibility of an LLT in an ab initio neural network model of water trained on density functional theory calculations with the SCAN exchange correlation functional. We conclusively show the existence of a first-order LLT and an associated critical point in the SCAN description of water, representing the first definitive computational evidence for an LLT in water from first principles.
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Affiliation(s)
- Thomas E Gartner
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | - Pablo M Piaggi
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | - Roberto Car
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
- Department of Physics, Princeton University, Princeton, New Jersey 08544, USA
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA
- Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, USA
| | | | - Pablo G Debenedetti
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
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35
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Grabowska J, Blazquez S, Sanz E, Zerón IM, Algaba J, Míguez JM, Blas FJ, Vega C. Solubility of Methane in Water: Some Useful Results for Hydrate Nucleation. J Phys Chem B 2022; 126:8553-8570. [PMID: 36222501 PMCID: PMC9623592 DOI: 10.1021/acs.jpcb.2c04867] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/16/2022] [Indexed: 11/30/2022]
Abstract
In this paper, the solubility of methane in water along the 400 bar isobar is determined by computer simulations using the TIP4P/Ice force field for water and a simple LJ model for methane. In particular, the solubility of methane in water when in contact with the gas phase and the solubility of methane in water when in contact with the hydrate has been determined. The solubility of methane in a gas-liquid system decreases as temperature increases. The solubility of methane in a hydrate-liquid system increases with temperature. The two curves intersect at a certain temperature that determines the triple point T3 at a certain pressure. We also determined T3 by the three-phase direct coexistence method. The results of both methods agree, and we suggest 295(2) K as the value of T3 for this system. We also analyzed the impact of curvature on the solubility of methane in water. We found that the presence of curvature increases the solubility in both the gas-liquid and hydrate-liquid systems. The change in chemical potential for the formation of hydrate is evaluated along the isobar using two different thermodynamic routes, obtaining good agreement between them. It is shown that the driving force for hydrate nucleation under experimental conditions is higher than that for the formation of pure ice when compared at the same supercooling. We also show that supersaturation (i.e., concentrations above those of the planar interface) increases the driving force for nucleation dramatically. The effect of bubbles can be equivalent to that of an additional supercooling of about 20 K. Having highly supersaturated homogeneous solutions makes possible the spontaneous formation of the hydrate at temperatures as high as 285 K (i.e., 10K below T3). The crucial role of the concentration of methane for hydrate formation is clearly revealed. Nucleation of the hydrate can be either impossible or easy and fast depending on the concentration of methane which seems to play the leading role in the understanding of the kinetics of hydrate formation.
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Affiliation(s)
- Joanna Grabowska
- Departamento
Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Department
of Physical Chemistry, Faculty of Chemistry and BioTechMed Center, Gdansk University of Technology, ul. Narutowicza 11/12, 80-233 Gdansk, Poland
| | - Samuel Blazquez
- Departamento
Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Eduardo Sanz
- Departamento
Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Iván M. Zerón
- Laboratorio
de Simulación Molecular y Química Computacional, CIQSO-Centro
de Investigación en Química Sostenible and Departamento
de Ciencias Integradas, Universidad de Huelva, 21006 Huelva, Spain
| | - Jesús Algaba
- Laboratorio
de Simulación Molecular y Química Computacional, CIQSO-Centro
de Investigación en Química Sostenible and Departamento
de Ciencias Integradas, Universidad de Huelva, 21006 Huelva, Spain
| | - José Manuel Míguez
- Laboratorio
de Simulación Molecular y Química Computacional, CIQSO-Centro
de Investigación en Química Sostenible and Departamento
de Ciencias Integradas, Universidad de Huelva, 21006 Huelva, Spain
| | - Felipe J. Blas
- Laboratorio
de Simulación Molecular y Química Computacional, CIQSO-Centro
de Investigación en Química Sostenible and Departamento
de Ciencias Integradas, Universidad de Huelva, 21006 Huelva, Spain
| | - Carlos Vega
- Departamento
Química Física I, Fac. Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
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