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Chaparro G, Müller EA. Simulation and Data-Driven Modeling of the Transport Properties of the Mie Fluid. J Phys Chem B 2024; 128:551-566. [PMID: 38181201 PMCID: PMC10801693 DOI: 10.1021/acs.jpcb.3c06813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/14/2023] [Accepted: 12/15/2023] [Indexed: 01/07/2024]
Abstract
This work reports the computation and modeling of the self-diffusivity (D*), shear viscosity (η*), and thermal conductivity (κ*) of the Mie fluid. The transport properties were computed using equilibrium molecular dynamics simulations for the Mie fluid with repulsive exponents (λr) ranging from 7 to 34 and at a fixed attractive exponent (λa) of 6 over the whole fluid density (ρ*) range and over a wide temperature (T*) range. The computed database consists of 17,212, 14,288, and 13,099 data points for self-diffusivity, shear viscosity, and thermal conductivity, respectively. The database is successfully validated against published simulation data. The above-mentioned transport properties are correlated using artificial neural networks (ANNs). Two modeling approaches were tested: a semiempirical formulation based on entropy scaling and an empirical formulation based on density and temperature as input variables. For the former, it was found that a unique formulation based on entropy scaling does not yield satisfactory results over the entire density range due to a divergent and incorrect scaling of the transport properties at low densities. For the latter empirical modeling approach, it was found that regularizing the data, e.g., modeling ρ*D* instead of D*, ln η* instead of η*, and ln κ* instead of κ*, as well as using the inverse of the temperature as an input feature, helps to ease the interpolation efforts of the artificial neural networks. The trained ANNs can model seen and unseen data over a wide range of density and temperature. Ultimately, the ANNs can be used alongside equations of state to regress effective force field parameters from volumetric and transport data.
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Affiliation(s)
- Gustavo Chaparro
- Department of Chemical Engineering,
Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
| | - Erich A. Müller
- Department of Chemical Engineering,
Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
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2
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Jirasek F, Hasse H. Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures. Annu Rev Chem Biomol Eng 2023; 14:31-51. [PMID: 36944250 DOI: 10.1146/annurev-chembioeng-092220-025342] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, ranging from molecular models over equations of state to models of excess properties. These well-established methods are currently being complemented by new methods from the field of machine learning (ML). This review focuses on the rapidly developing interface between these two approaches and gives a structured overview of how physical modeling and ML can be combined to yield hybrid models. We illustrate the different options with examples from recent research and give an outlook on future developments.
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Affiliation(s)
- Fabian Jirasek
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern, Germany; ,
| | - Hans Hasse
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern, Germany; ,
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3
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Leverant CJ, Greathouse JA, Harvey JA, Alam TM. Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids. J Chem Theory Comput 2023. [PMID: 37192538 DOI: 10.1021/acs.jctc.2c01040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Diffusion properties of bulk fluids have been predicted using empirical expressions and machine learning (ML) models, suggesting that predictions of diffusion also should be possible for fluids in confined environments. The ability to quickly and accurately predict diffusion in porous materials would enable new discoveries and spur development in relevant technologies such as separations, catalysis, batteries, and subsurface applications. In this work, we apply artificial neural network (ANN) models to predict the simulated self-diffusion coefficients of real liquids in both bulk and pore environments. The training data sets were generated from molecular dynamics (MD) simulations of Lennard-Jones particles representing a diverse set of 14 molecules ranging from ammonia to dodecane over a range of liquid pressures and temperatures. Planar, cylindrical, and hexagonal pore models consisted of walls composed of carbon atoms. Our simple model for these liquids was primarily used to generate ANN training data, but the simulated self-diffusion coefficients of bulk liquids show excellent agreement with experimental diffusion coefficients. ANN models based on simple descriptors accurately reproduced the MD diffusion data for both bulk and confined liquids, including the trend of increased mobility in large pores relative to the corresponding bulk liquid.
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Affiliation(s)
- Calen J Leverant
- Nanoscale Sciences Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Jeffery A Greathouse
- Nuclear Waste Disposal Research & Analysis Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Jacob A Harvey
- Geochemistry Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Todd M Alam
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
- ACC Consulting New Mexico, Cedar Crest, New Mexico 87008, United States
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Zêzere B, Fonseca TVB, Portugal I, Simões MMQ, Silva CM, Gomes JRB. Influence of Ethanol Parametrization on Diffusion Coefficients Using OPLS-AA Force Field. Int J Mol Sci 2023; 24:ijms24087316. [PMID: 37108479 PMCID: PMC10138630 DOI: 10.3390/ijms24087316] [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: 02/16/2023] [Revised: 04/03/2023] [Accepted: 04/08/2023] [Indexed: 04/29/2023] Open
Abstract
Molecular dynamics simulations employing the all-atom optimized potential for liquid simulations (OPLS-AA) force field were performed for determining self-diffusion coefficients (D11) of ethanol and tracer diffusion coefficients (D12) of solutes in ethanol at several temperature and pressure conditions. For simulations employing the original OPLS-AA diameter of ethanol's oxygen atom (σOH), calculated and experimental diffusivities of protic solutes differed by more than 25%. To correct this behavior, the σOH was reoptimized using the experimental D12 of quercetin and of gallic acid in liquid ethanol as benchmarks. A substantial improvement of the calculated diffusivities was found by changing σOH from its original value (0.312 nm) to 0.306 nm, with average absolute relative deviations (AARD) of 3.71% and 4.59% for quercetin and gallic acid, respectively. The new σOH value was further tested by computing D12 of ibuprofen and butan-1-ol in liquid ethanol with AARDs of 1.55% and 4.81%, respectively. A significant improvement was also obtained for the D11 of ethanol with AARD = 3.51%. It was also demonstrated that in the case of diffusion coefficients of non-polar solutes in ethanol, the original σOH=0.312 nm should be used for better agreement with experiment. If equilibrium properties such as enthalpy of vaporization and density are estimated, the original diameter should be once again adopted.
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Affiliation(s)
- Bruno Zêzere
- CICECO-Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - Tiago V B Fonseca
- CICECO-Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - Inês Portugal
- CICECO-Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - Mário M Q Simões
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - Carlos M Silva
- CICECO-Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - José R B Gomes
- CICECO-Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
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Yasuda I, Kobayashi Y, Endo K, Hayakawa Y, Fujiwara K, Yajima K, Arai N, Yasuoka K. Combining Molecular Dynamics and Machine Learning to Analyze Shear Thinning for Alkane and Globular Lubricants in the Low Shear Regime. ACS APPLIED MATERIALS & INTERFACES 2023; 15:8567-8578. [PMID: 36715349 DOI: 10.1021/acsami.2c16366] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Lubricants with desirable frictional properties are important in achieving an energy-saving society. Lubricants at the interfaces of mechanical components are confined under high shear rates and pressures and behave quite differently from the bulk material. Computational approaches such as nonequilibrium molecular dynamics (NEMD) simulations have been performed to probe the molecular behavior of lubricants. However, the low-shear-velocity regions of the materials have rarely been simulated owing to the expensive calculations necessary to do so, and the molecular dynamics under shear velocities comparable with that in the experiments are not clearly understood. In this study, we performed NEMD simulations of extremely confined lubricants, i.e., two molecular layers for four types of lubricants confined in mica walls, under shear velocities from 0.001 to 1 m/s. While we confirmed shear thinning, the velocity profiles could not show the flow behavior when the shear velocity was much slower than thermal fluctuations. Therefore, we used an unsupervised machine learning approach to detect molecular movements that contribute to shear thinning. First, we extracted the simple features of molecular movements from large amounts of MD data, which were found to correlate with the effective viscosity. Subsequently, the extracted features were interpreted by examining the trajectories contributing to these features. The magnitude of diffusion corresponded to the viscosity, and the location of slips that varied depending on the spherical and chain lubricants was irrelevant. Finally, we attempted to apply a modified Stokes-Einstein relation at equilibrium to the nonequilibrium and confined systems. While systems with low shear rates obeyed the relation sufficiently, large deviations were observed under large shear rates.
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Affiliation(s)
- Ikki Yasuda
- Department of Mechanical Engineering, Keio University, Yokohama, Kanagawa223-8522, Japan
| | - Yusei Kobayashi
- Department of Mechanical Engineering, Keio University, Yokohama, Kanagawa223-8522, Japan
| | - Katsuhiro Endo
- Department of Mechanical Engineering, Keio University, Yokohama, Kanagawa223-8522, Japan
| | - Yoshihiro Hayakawa
- Department of General Engineering, National Institute of Technology, Sendai College, Sendai, Miyagi989-3128, Japan
| | - Kazuhiko Fujiwara
- Department of General Engineering, National Institute of Technology, Sendai College, Sendai, Miyagi989-3128, Japan
| | - Kuniaki Yajima
- Department of General Engineering, National Institute of Technology, Sendai College, Sendai, Miyagi989-3128, Japan
| | - Noriyoshi Arai
- Department of Mechanical Engineering, Keio University, Yokohama, Kanagawa223-8522, Japan
| | - Kenji Yasuoka
- Department of Mechanical Engineering, Keio University, Yokohama, Kanagawa223-8522, Japan
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Zeng F, Wan R, Xiao Y, Song F, Peng C, Liu H. Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c03342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Fazhan Zeng
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
| | - Ren Wan
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
| | - Yongjun Xiao
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
| | - Fan Song
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
| | - Changjun Peng
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
| | - Honglai Liu
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
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Allers JP, Keth J, Alam TM. Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks. J Phys Chem B 2022; 126:4555-4564. [PMID: 35675158 DOI: 10.1021/acs.jpcb.2c01723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.
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Affiliation(s)
- Joshua P Allers
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States.,Virtual Technologies and Engineering, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Jane Keth
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Todd M Alam
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States.,ACC Consulting New Mexico, Cedar Crest, New Mexico 87008, United States
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