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Schwerdtfeger P, Wales DJ. 100 Years of the Lennard-Jones Potential. J Chem Theory Comput 2024; 20:3379-3405. [PMID: 38669689 DOI: 10.1021/acs.jctc.4c00135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
It is now 100 years since Lennard-Jones published his first paper introducing the now famous potential that bears his name. It is therefore timely to reflect on the many achievements, as well as the limitations, of this potential in the theory of atomic and molecular interactions, where applications range from descriptions of intermolecular forces to molecules, clusters, and condensed matter.
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Affiliation(s)
- Peter Schwerdtfeger
- Centre for Theoretical Chemistry and Physics, The New Zealand Institute for Advanced Study, Massey University Auckland, Private Bag 102904, Auckland 0745, New Zealand
| | - David J Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
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2
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Liu H, Li L, Wei Z, Smedskjaer MM, Zheng XR, Bauchy M. De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304834. [PMID: 38269856 PMCID: PMC10987143 DOI: 10.1002/advs.202304834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 12/22/2023] [Indexed: 01/26/2024]
Abstract
Architected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so-termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse-resolution, ordered-pattern design space. Here, combining high-throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some intriguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight-yet-stiff cellular materials featuring a theoretical limit of linear stiffness-density scaling, whose structural disorder-rather than order-is key to reduce the scaling exponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in-between directional and non-directional bonding such as covalent and ionic bonds, modest bond directionality is most likely to promotes disordered packing of polyhedral, stretching-dominated structures responsible for the formation of metamaterials. This work pioneers a bottom-down atomistic scheme to design mechanical metamaterials formatted disorderly, unlocking a largely untapped field in leveraging structural disorder in devising metamaterials atomistically and, potentially, generic to conventional upscaled designs.
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Affiliation(s)
- Han Liu
- SOlids inFormaTics AI‐Laboratory (SOFT‐AI‐Lab)College of Polymer Science and EngineeringSichuan UniversityChengdu610065China
- AIMSOLID ResearchWuhan430223China
| | - Liantang Li
- SOlids inFormaTics AI‐Laboratory (SOFT‐AI‐Lab)College of Polymer Science and EngineeringSichuan UniversityChengdu610065China
- AIMSOLID ResearchWuhan430223China
| | - Zhenhua Wei
- Department of Ocean Science and EngineeringSouthern University of Science and TechnologyShenzhen518055China
| | | | - Xiaoyu Rayne Zheng
- Department of Material Science and EngineeringUniversity of California BerkeleyBerkeleyCA94720USA
| | - Mathieu Bauchy
- Physics of Amorphous and Inorganic Solids Laboratory (PARISlab)Department of Civil and Environmental EngineeringUniversity of CaliforniaLos AngelesCA90095USA
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Liu H, Huang Z, Schoenholz SS, Cubuk ED, Smedskjaer MM, Sun Y, Wang W, Bauchy M. Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator. MATERIALS HORIZONS 2023; 10:3416-3428. [PMID: 37382413 DOI: 10.1039/d3mh00028a] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Many-body dynamics of atoms such as glass dynamics is generally governed by complex (and sometimes unknown) physics laws. This challenges the construction of atom dynamics simulations that both (i) capture the physics laws and (ii) run with little computation cost. Here, based on graph neural network (GNN), we introduce an observation-based graph network (OGN) framework to "bypass all physics laws" to simulate complex glass dynamics solely from their static structure. By taking the example of molecular dynamics (MD) simulations, we successfully apply the OGN to predict atom trajectories evolving up to a few hundred timesteps and ranging over different families of complex atomistic systems, which implies that the atom dynamics is largely encoded in their static structure in disordered phases and, furthermore, allows us to explore the capacity of OGN simulations that is potentially generic to many-body dynamics. Importantly, unlike traditional numerical simulations, the OGN simulations bypass the numerical constraint of small integration timestep by a multiplier of ≥5 to conserve energy and momentum until hundreds of timesteps, thus leapfrogging the execution speed of MD simulations for a modest timescale.
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Affiliation(s)
- Han Liu
- SOlids inFormaTics AI-Laboratory (SOFT-AI-Lab), College of Polymer Science and Engineering, Sichuan University, Chengdu 610065, China.
| | - Zijie Huang
- Department of Computer Science, University of California, Los Angeles, California, 90095, USA
| | | | - Ekin D Cubuk
- Brain Team, Google Research, Mountain View, California, 94043, USA
| | - Morten M Smedskjaer
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
| | - Yizhou Sun
- Department of Computer Science, University of California, Los Angeles, California, 90095, USA
| | - Wei Wang
- Department of Computer Science, University of California, Los Angeles, California, 90095, USA
| | - Mathieu Bauchy
- Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, California, 90095, USA.
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McSloy A, Fan G, Sun W, Hölzer C, Friede M, Ehlert S, Schütte NE, Grimme S, Frauenheim T, Aradi B. TBMaLT, a flexible toolkit for combining tight-binding and machine learning. J Chem Phys 2023; 158:034801. [PMID: 36681630 DOI: 10.1063/5.0132892] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Tight-binding approaches, especially the Density Functional Tight-Binding (DFTB) and the extended tight-binding schemes, allow for efficient quantum mechanical simulations of large systems and long-time scales. They are derived from ab initio density functional theory using pragmatic approximations and some empirical terms, ensuring a fine balance between speed and accuracy. Their accuracy can be improved by tuning the empirical parameters using machine learning techniques, especially when information about the local environment of the atoms is incorporated. As the significant quantum mechanical contributions are still provided by the tight-binding models, and only short-ranged corrections are fitted, the learning procedure is typically shorter and more transferable as it were with predicting the quantum mechanical properties directly with machine learning without an underlying physically motivated model. As a further advantage, derived quantum mechanical quantities can be calculated based on the tight-binding model without the need for additional learning. We have developed the open-source framework-Tight-Binding Machine Learning Toolkit-which allows the easy implementation of such combined approaches. The toolkit currently contains layers for the DFTB method and an interface to the GFN1-xTB Hamiltonian, but due to its modular structure and its well-defined interfaces, additional atom-based schemes can be implemented easily. We are discussing the general structure of the framework, some essential implementation details, and several proof-of-concept applications demonstrating the perspectives of the combined methods and the functionality of the toolkit.
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Affiliation(s)
- A McSloy
- Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - G Fan
- Bremen Center of Computational Materials Science, University of Bremen, 28359 Bremen, Germany
| | - W Sun
- Bremen Center of Computational Materials Science, University of Bremen, 28359 Bremen, Germany
| | - C Hölzer
- Mulliken Center for Theoretical Chemistry, University of Bonn, 53115 Bonn, Germany
| | - M Friede
- Mulliken Center for Theoretical Chemistry, University of Bonn, 53115 Bonn, Germany
| | - S Ehlert
- Mulliken Center for Theoretical Chemistry, University of Bonn, 53115 Bonn, Germany
| | - N-E Schütte
- Bremen Center of Computational Materials Science, University of Bremen, 28359 Bremen, Germany
| | - S Grimme
- Mulliken Center for Theoretical Chemistry, University of Bonn, 53115 Bonn, Germany
| | - T Frauenheim
- Bremen Center of Computational Materials Science, University of Bremen, 28359 Bremen, Germany
| | - B Aradi
- Bremen Center of Computational Materials Science, University of Bremen, 28359 Bremen, Germany
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Avula NVS, Karmakar A, Kumar R, Balasubramanian S. Efficient Parametrization of Force Field for the Quantitative Prediction of the Physical Properties of Ionic Liquid Electrolytes. J Chem Theory Comput 2021; 17:4274-4290. [PMID: 34097391 DOI: 10.1021/acs.jctc.1c00268] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The prediction of transport properties of room-temperature ionic liquids from nonpolarizable force field-based simulations has long been a challenge. The uniform charge scaling method has been widely used to improve the agreement with the experiment by incorporating the polarizability and charge transfer effects in an effective manner. While this method improves the performance of the force fields, this prescription is ad hoc in character; further, a quantitative prediction is still not guaranteed. In such cases, the nonbonded interaction parameters too need to be refined, which requires significant effort. In this work, we propose a three-step semiautomated refinement procedure based on (1) atomic site charges obtained from quantum calculations of the bulk condensed phase; (2) quenched Monte Carlo optimizer to shortlist suitable force field candidates, which are then tested using pilot simulations; and (3) manual refinement to further improve the accuracy of the force field. The strategy is designed in a sequential manner with each step improving the accuracy over the previous step, allowing the users to invest the effort commensurate with the desired accuracy of the refined force field. The refinement procedure is applied on N,N-diethyl-N-methyl-N-(2-methoxyethyl)ammonium bis(trifluoromethanesulfonyl)imide (DEME-TFSI), a front-runner as an electrolyte for electric double-layer capacitors and single-molecule-based devices. The transferability of the refined force field is tested on N,N-dimethyl-N-ethyl-N-methoxyethoxyethylammonium bis(trifluoromethanesulfonyl)imide (N112,2O2O1-TFSI). The refined force field is found to be better at predicting both structural and transport properties compared to the uniform charge scaling procedure, which showed a discrepancy in the X-ray structure factor. The refined force field showed quantitative agreement with structural (density and X-ray structure factor) and transport properties-diffusion coefficients, ionic conductivity, and shear viscosity over a wide temperature range, building a case for the wide adoption of the procedure.
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Affiliation(s)
- Nikhil V S Avula
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - Anwesa Karmakar
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - Rahul Kumar
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - Sundaram Balasubramanian
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
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Ceriotti M, Clementi C, Anatole von Lilienfeld O. Machine learning meets chemical physics. J Chem Phys 2021; 154:160401. [PMID: 33940847 DOI: 10.1063/5.0051418] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Over recent years, the use of statistical learning techniques applied to chemical problems has gained substantial momentum. This is particularly apparent in the realm of physical chemistry, where the balance between empiricism and physics-based theory has traditionally been rather in favor of the latter. In this guest Editorial for the special topic issue on "Machine Learning Meets Chemical Physics," a brief rationale is provided, followed by an overview of the topics covered. We conclude by making some general remarks.
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Affiliation(s)
- Michele Ceriotti
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Cecilia Clementi
- Department of Physics, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany
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Christensen R, Sørensen SS, Liu H, Li K, Bauchy M, Smedskjaer MM. Interatomic potential parameterization using particle swarm optimization: Case study of glassy silica. J Chem Phys 2021; 154:134505. [PMID: 33832276 DOI: 10.1063/5.0041183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Classical molecular dynamics simulations of glassy materials rely on the availability of accurate yet computationally efficient interatomic force fields. The parameterization of new potentials remains challenging due to the non-convex nature of the accompanying optimization problem, which renders the traditional optimization methods inefficient or subject to bias. In this study, we present a new parameterization method based on particle swarm optimization (PSO), which is a stochastic population-based optimization method. Using glassy silica as a case study, we introduce two interatomic potentials using PSO, which are parameterized so as to match structural features obtained from ab initio simulations and experimental neutron diffraction data. We find that the PSO algorithm is highly efficient at searching for and identifying viable potential parameters that reproduce the structural features used as the target in the parameterization. The presented approach is very general and can be easily applied to other interatomic potential parameterization schemes.
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Affiliation(s)
- Rasmus Christensen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Søren S Sørensen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Han Liu
- Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, USA
| | - Kevin Li
- Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, USA
| | - Mathieu Bauchy
- Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, USA
| | - Morten M Smedskjaer
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
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