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For: Liu H, Li Y, Fu Z, Li K, Bauchy M. Exploring the landscape of Buckingham potentials for silica by machine learning: Soft vs hard interatomic forcefields. J Chem Phys 2020;152:051101. [DOI: 10.1063/1.5136041] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]  Open
Number Cited by Other Article(s)
1
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]
2
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]
3
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]
4
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
5
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]
6
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
7
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
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