1
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Soshnikov A, Lindsey R, Kulkarni A, Goldman N. A reactive molecular dynamics model for uranium/hydrogen containing systems. J Chem Phys 2024; 160:094117. [PMID: 38450731 DOI: 10.1063/5.0183610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/09/2024] [Indexed: 03/08/2024] Open
Abstract
Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. We show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.
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Affiliation(s)
- Artem Soshnikov
- Department of Chemical Engineering, University of California, Davis, California 95616, USA
| | - Rebecca Lindsey
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Ambarish Kulkarni
- Department of Chemical Engineering, University of California, Davis, California 95616, USA
| | - Nir Goldman
- Department of Chemical Engineering, University of California, Davis, California 95616, USA
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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2
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Lindsey RK, Bastea S, Lyu Y, Hamel S, Goldman N, Fried LE. Chemical evolution in nitrogen shocked beyond the molecular stability limit. J Chem Phys 2023; 159:084502. [PMID: 37622598 DOI: 10.1063/5.0157238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 07/24/2023] [Indexed: 08/26/2023] Open
Abstract
Evolution of nitrogen under shock compression up to 100 GPa is revisited via molecular dynamics simulations using a machine-learned interatomic potential. The model is shown to be capable of recovering the structure, dynamics, speciation, and kinetics in hot compressed liquid nitrogen predicted by first-principles molecular dynamics, as well as the measured principal shock Hugoniot and double shock experimental data, albeit without shock cooling. Our results indicate that a purely molecular dissociation description of nitrogen chemistry under shock compression provides an incomplete picture and that short oligomers form in non-negligible quantities. This suggests that classical models representing the shock dissociation of nitrogen as a transition to an atomic fluid need to be revised to include reversible polymerization effects.
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Affiliation(s)
- Rebecca K Lindsey
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Sorin Bastea
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Yanjun Lyu
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Sebastien Hamel
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Nir Goldman
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
- Department of Chemical Engineering, University of California, Davis, California 95616, USA
| | - Laurence E Fried
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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3
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Goldman N, Fried LE, Lindsey RK, Pham CH, Dettori R. Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials. J Chem Phys 2023; 158:144112. [PMID: 37061479 DOI: 10.1063/5.0141616] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2023] Open
Abstract
Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly, and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.
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Affiliation(s)
- Nir Goldman
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Laurence E Fried
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Rebecca K Lindsey
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - C Huy Pham
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - R Dettori
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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4
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Pham CH, Lindsey RK, Fried LE, Goldman N. High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set. J Phys Chem Lett 2022; 13:2934-2942. [PMID: 35343698 DOI: 10.1021/acs.jpclett.2c00453] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ∼0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.
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Affiliation(s)
- Cong Huy Pham
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Rebecca K Lindsey
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Laurence E Fried
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Nir Goldman
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
- Department of Chemical Engineering, University of California, Davis, California 95616, United States
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5
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Lindsey RK, Huy Pham C, Goldman N, Bastea S, Fried LE. Machine‐Learning a Solution for Reactive Atomistic Simulations of Energetic Materials. PROPELLANTS EXPLOSIVES PYROTECHNICS 2022. [DOI: 10.1002/prep.202200001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Rebecca K. Lindsey
- Physical and Life Sciences Directorate Lawrence Livermore National Laboratory Livermore California 94550 USA
| | - Cong Huy Pham
- Physical and Life Sciences Directorate Lawrence Livermore National Laboratory Livermore California 94550 USA
| | - Nir Goldman
- Physical and Life Sciences Directorate Lawrence Livermore National Laboratory Livermore California 94550 USA
- Department of Chemical Engineering University of California, Davis Davis California 95616 USA
| | - Sorin Bastea
- Physical and Life Sciences Directorate Lawrence Livermore National Laboratory Livermore California 94550 USA
| | - Laurence E. Fried
- Physical and Life Sciences Directorate Lawrence Livermore National Laboratory Livermore California 94550 USA
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6
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Chemistry-mediated Ostwald ripening in carbon-rich C/O systems at extreme conditions. Nat Commun 2022; 13:1424. [PMID: 35301293 PMCID: PMC8931168 DOI: 10.1038/s41467-022-29024-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/21/2022] [Indexed: 11/08/2022] Open
Abstract
There is significant interest in establishing a capability for tailored synthesis of next-generation carbon-based nanomaterials due to their broad range of applications and high degree of tunability. High pressure (e.g., shockwave-driven) synthesis holds promise as an effective discovery method, but experimental challenges preclude elucidating the processes governing nanocarbon production from carbon-rich precursors that could otherwise guide efforts through the prohibitively expansive design space. Here we report findings from large scale atomistically-resolved simulations of carbon condensation from C/O mixtures subjected to extreme pressures and temperatures, made possible by machine-learned reactive interatomic potentials. We find that liquid nanocarbon formation follows classical growth kinetics driven by Ostwald ripening (i.e., growth of large clusters at the expense of shrinking small ones) and obeys dynamical scaling in a process mediated by carbon chemistry in the surrounding reactive fluid. The results provide direct insight into carbon condensation in a representative system and pave the way for its exploration in higher complexity organic materials. They also suggest that simulations using machine-learned interatomic potentials could eventually be employed as in-silico design tools for new nanomaterials. Modelling the growth of carbon nanoclusters in shock experiments is computationally demanding. Here the authors employ a machine-learned reactive interatomic model to perform large-scale simulations of nanocarbon formation from prototypical shocked C/O-containing precursor.
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7
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Dettmann MA, Cavalcante LSR, Magdaleno C, Masalkovaitė K, Vong D, Dull JT, Rand BP, Daemen LL, Goldman N, Faller R, Moulé AJ. Comparing the Expense and Accuracy of Methods to Simulate Atomic Vibrations in Rubrene. J Chem Theory Comput 2021; 17:7313-7320. [PMID: 34818006 DOI: 10.1021/acs.jctc.1c00747] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder in metal-organic frameworks. Currently, there are several methods for predicting these vibrations using simulations, but the accuracy-efficiency tradeoffs have not been examined in depth. In this study, rubrene is used as a model system to predict atomic vibrational properties using six different simulation methods: density functional theory, density functional tight binding, density functional tight binding with a Chebyshev polynomial-based correction, a trained machine learning model, a pretrained machine learning model called ANI-1, and a classical forcefield model. The accuracy of each method is evaluated by comparison to the experimental inelastic neutron scattering spectrum. All methods discussed here show some accuracy across a wide energy region, though the Chebyshev-corrected tight-binding method showed the optimal combination of high accuracy with low expense. We then offer broad simulation guidelines to yield efficient, accurate results for inelastic neutron scattering spectrum prediction.
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Affiliation(s)
- Makena A Dettmann
- University of California Davis, Davis, California 95616, United States
| | | | - Corina Magdaleno
- University of California Davis, Davis, California 95616, United States
| | | | - Daniel Vong
- University of California Davis, Davis, California 95616, United States
| | - Jordan T Dull
- Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Barry P Rand
- Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Luke L Daemen
- Oak Ridge National Lab, Oak Ridge, Tennessee 37831, United States
| | - Nir Goldman
- University of California Davis, Davis, California 95616, United States.,Lawrence Livermore National Lab, Livermore, California 94550, United States
| | - Roland Faller
- University of California Davis, Davis, California 95616, United States
| | - Adam J Moulé
- University of California Davis, Davis, California 95616, United States
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8
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Cavalcante LSR, Daemen LL, Goldman N, Moulé AJ. Davis Computational Spectroscopy Workflow-From Structure to Spectra. J Chem Inf Model 2021; 61:4486-4496. [PMID: 34449225 DOI: 10.1021/acs.jcim.1c00688] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We describe an automated workflow that connects a series of atomic simulation tools to investigate the relationship between atomic structure, lattice dynamics, materials properties, and inelastic neutron scattering (INS) spectra. Starting from the atomic simulation environment (ASE) as an interface, we demonstrate the use of a selection of calculators, including density functional theory (DFT) and density functional tight binding (DFTB), to optimize the structures and calculate interatomic force constants. We present the use of our workflow to compute the phonon frequencies and eigenvectors, which are required to accurately simulate the INS spectra in crystalline solids like diamond and graphite as well as molecular solids like rubrene. We have also implemented a machine-learning force field based on Chebyshev polynomials called the Chebyshev interaction model for efficient simulation (ChIMES) to improve the accuracy of the DFTB simulations. We then explore the transferability of our DFTB/ChIMES models by comparing simulations derived from different training sets. We show that DFTB/ChIMES demonstrates ∼100× reduction in computational expense while retaining most of the accuracy of DFT as well as yielding high accuracy for different materials outside of our training sets. The DFTB/ChIMES method within the workflow expands the possibilities to use simulations to accurately predict materials properties of increasingly complex structures that would be unfeasible with ab initio methods.
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Affiliation(s)
- L S R Cavalcante
- Department of Chemical Engineering, University of California, Davis, California 95616, United States
| | - Luke L Daemen
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Nir Goldman
- Department of Chemical Engineering, University of California, Davis, California 95616, United States.,Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Adam J Moulé
- Department of Chemical Engineering, University of California, Davis, California 95616, United States
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9
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Goldman N, Kweon KE, Sadigh B, Heo TW, Lindsey RK, Pham CH, Fried LE, Aradi B, Holliday K, Jeffries JR, Wood BC. Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields. J Chem Theory Comput 2021; 17:4435-4448. [PMID: 34128678 DOI: 10.1021/acs.jctc.1c00172] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH2 as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.
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Affiliation(s)
- Nir Goldman
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.,Department of Chemical Engineering, University of California, Davis, Davis, California 95616, United States
| | - Kyoung E Kweon
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Babak Sadigh
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Tae Wook Heo
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Rebecca K Lindsey
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - C Huy Pham
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Laurence E Fried
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Bálint Aradi
- Bremen Center for Computational Materials Science, Universität Bremen, P.O.B. 330440, Bremen D-28334, Germany
| | - Kiel Holliday
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Jason R Jeffries
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Brandon C Wood
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
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10
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Lindsey RK, Bastea S, Goldman N, Fried LE. Investigating 3,4-bis(3-nitrofurazan-4-yl)furoxan detonation with a rapidly tuned density functional tight binding model. J Chem Phys 2021; 154:164115. [PMID: 33940855 DOI: 10.1063/5.0047800] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
We describe a machine learning approach to rapidly tune density functional tight binding models for the description of detonation chemistry in organic molecular materials. Resulting models enable simulations on the several 10s of ps scales characteristic to these processes, with "quantum-accuracy." We use this approach to investigate early shock chemistry in 3,4-bis(3-nitrofurazan-4-yl)furoxan, a hydrogen-free energetic material known to form onion-like nanocarbon particulates following detonation. We find that the ensuing chemistry is significantly characterized by the formation of large CxNyOz species, which are likely precursors to the experimentally observed carbon condensates. Beyond utility as a means of investigating detonation chemistry, the present approach can be used to generate quantum-based reference data for the development of full machine-learned interatomic potentials capable of simulation on even greater time and length scales, i.e., for applications where characteristic time scales exceed the reach of methods including Kohn-Sham density functional theory, which are commonly used for reference data generation.
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Affiliation(s)
- Rebecca K Lindsey
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Sorin Bastea
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Nir Goldman
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Laurence E Fried
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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11
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Allen AEA, Dusson G, Ortner C, Csányi G. Atomic permutationally invariant polynomials for fitting molecular force fields. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abd51e] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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12
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Pham CH, Lindsey RK, Fried LE, Goldman N. Calculation of the detonation state of HN3 with quantum accuracy. J Chem Phys 2020; 153:224102. [DOI: 10.1063/5.0029011] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Cong Huy Pham
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Rebecca K. Lindsey
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Laurence E. Fried
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Nir Goldman
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
- Department of Chemical Engineering, University of California, Davis, California 95616, USA
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13
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Lindsey RK, Fried LE, Goldman N, Bastea S. Active learning for robust, high-complexity reactive atomistic simulations. J Chem Phys 2020; 153:134117. [DOI: 10.1063/5.0021965] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Rebecca K. Lindsey
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Laurence E. Fried
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Nir Goldman
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
- Department of Chemical Engineering, University of California, Davis, California 95616, USA
| | - Sorin Bastea
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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14
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Lindsey RK, Goldman N, Fried LE, Bastea S. Many-body reactive force field development for carbon condensation in C/O systems under extreme conditions. J Chem Phys 2020; 153:054103. [DOI: 10.1063/5.0012840] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Affiliation(s)
- Rebecca K. Lindsey
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Nir Goldman
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
- Department of Chemical Engineering, University of California, Davis, California 95616, USA
| | - Laurence E. Fried
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Sorin Bastea
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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15
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Jiang L, Rogers DM, Hirst JD, Do H. Force Fields for Macromolecular Assemblies Containing Diketopyrrolopyrrole and Thiophene. J Chem Theory Comput 2020; 16:5150-5162. [DOI: 10.1021/acs.jctc.0c00399] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Ling Jiang
- Department of Chemical and Environmental Engineering, University of Nottingham—Ningbo China, Ningbo 315100, China
| | - David M. Rogers
- School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Jonathan D. Hirst
- School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Hainam Do
- Department of Chemical and Environmental Engineering, University of Nottingham—Ningbo China, Ningbo 315100, China
- New Materials Institute, University of Nottingham—Ningbo China, Ningbo 315042, China
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16
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Jadrich RB, Leiding JA. Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials. J Phys Chem B 2020; 124:5488-5497. [DOI: 10.1021/acs.jpcb.0c03738] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Ryan B. Jadrich
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Jeffery A. Leiding
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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17
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Dantanarayana V, Nematiaram T, Vong D, Anthony JE, Troisi A, Nguyen Cong K, Goldman N, Faller R, Moulé AJ. Predictive Model of Charge Mobilities in Organic Semiconductor Small Molecules with Force-Matched Potentials. J Chem Theory Comput 2020; 16:3494-3503. [DOI: 10.1021/acs.jctc.0c00211] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Varuni Dantanarayana
- Department of Chemistry, University of California—Davis, Davis, California 95616, United States
- Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Tahereh Nematiaram
- Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Daniel Vong
- Department of Materials Science and Engineering, University of California—Davis, Davis, California 95616, United States
| | - John E. Anthony
- Department of Chemistry, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Alessandro Troisi
- Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Kien Nguyen Cong
- Department of Physics, University of South Florida, Tampa, Florida 33620, United States
| | - Nir Goldman
- Lawrence Livermore National Laboratory, Livermore, California 94550, United States
- Department of Chemical Engineering, University of California—Davis - Davis, California 95616, United States
| | - Roland Faller
- Department of Chemical Engineering, University of California—Davis - Davis, California 95616, United States
| | - Adam J. Moulé
- Department of Chemical Engineering, University of California—Davis - Davis, California 95616, United States
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18
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Force Matching Approaches to Extend Density Functional Theory to Large Time and Length Scales. COMPUTATIONAL APPROACHES FOR CHEMISTRY UNDER EXTREME CONDITIONS 2019. [DOI: 10.1007/978-3-030-05600-1_4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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