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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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Alam TM, Allers JP, Leverant CJ, Harvey JA. Symbolic regression development of empirical equations for diffusion in Lennard-Jones fluids. J Chem Phys 2022; 157:014503. [DOI: 10.1063/5.0093658] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Symbolic regression (SR) with a multi-gene genetic program has been used to elucidate new empirical equations describing diffusion in Lennard-Jones (LJ) fluids. Examples include equations to predict self-diffusion in pure LJ fluids and equations describing the finite-size correction for self-diffusion in binary LJ fluids. The performance of the SR-obtained equations was compared to that of both the existing empirical equations in the literature and to the results from artificial neural net (ANN) models recently reported. It is found that the SR equations have improved predictive performance in comparison to the existing empirical equations, even though employing a smaller number of adjustable parameters, but show an overall reduced performance in comparison to more extensive ANNs.
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Affiliation(s)
- Todd M. Alam
- ACC Consulting New Mexico, Cedar Crest, New Mexico 87008, USA
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | - Joshua P. Allers
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | - Calen J. Leverant
- Department of WMD Threats and Aerosol Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
- Department of Chemical Engineering, University of Florida, Gainesville, Florida 32611, USA
| | - Jacob A. Harvey
- Geochemistry Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
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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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Zhao X, Luo T, Jin H. Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c00017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Xiao Zhao
- State Key Laboratory of Multiphase Flow in Power Engineering (SKLMF), Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Tengfei Luo
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Hui Jin
- State Key Laboratory of Multiphase Flow in Power Engineering (SKLMF), Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
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Desbiens N, Arnault P, Weens W, Dubois V, Perrin G. Bootstrapping time correlation functions of molecular dynamics. Phys Rev E 2021; 104:055310. [PMID: 34942746 DOI: 10.1103/physreve.104.055310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 11/04/2021] [Indexed: 11/07/2022]
Abstract
Molecular dynamics is often considered as a numerical experiment. The error bars on the results are therefore mandatory, but sometimes difficult to determine and computationally demanding. As a low-cost approach, we describe the application of the bootstrap (BS) method to the quantification of uncertainties pertaining to the time correlation functions. We chose the autocorrelation functions of velocity and interdiffusion current for a binary ionic mixture as a test bed, and we assessed the merit of the Darken approximation relating both of them. The intrinsic errors related to phase space sampling is investigated comparing the BS method with the reference method of replica. We also study how the BS method can assist in addressing the finite-size effects.
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Affiliation(s)
| | | | - William Weens
- CEA, DAM, DIF, 91297 Arpajon, France.,Laboratoire en Informatique Haute Performance pour le Calcul et la Simulation, 91680 Bruyères-le-Châtel, France
| | | | - Guillaume Perrin
- COSYS, Université Gustave Eiffel, 77420 Champs-sur-Marne, France
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Allers JP, Priest CW, Greathouse JA, Alam TM. Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids. J Phys Chem B 2021; 125:12990-13002. [PMID: 34793167 DOI: 10.1021/acs.jpcb.1c07092] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.
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Affiliation(s)
- Joshua P Allers
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Chad W Priest
- Geochemistry Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Jeffery A Greathouse
- 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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Allers JP, Garzon FH, Alam TM. Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes. Phys Chem Chem Phys 2021; 23:4615-4623. [PMID: 33620369 DOI: 10.1039/d0cp06693a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.
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Affiliation(s)
- Joshua P Allers
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, NM 87185, USA.
| | - Fernando H Garzon
- Advanced Materials Laboratory, Sandia National Laboratories, Albuquerque, NM 87185, USA and Center of Micro-Engineered Materials, University of New Mexico, Albuquerque, NM 87106, USA
| | - Todd M Alam
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, NM 87185, USA.
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