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Goodwin ZAH, Wenny MB, Yang JH, Cepellotti A, Ding J, Bystrom K, Duschatko BR, Johansson A, Sun L, Batzner S, Musaelian A, Mason JA, Kozinsky B, Molinari N. Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials. J Phys Chem Lett 2024; 15:7539-7547. [PMID: 39023916 DOI: 10.1021/acs.jpclett.4c01942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as "designer solvents" as they can be mixed to precisely tailor the physiochemical properties. As using machine learning interatomic potentials (MLIPs) to simulate ILs is still relatively unexplored, several questions need to be answered to see if MLIPs can be transformative for ILs. Since ILs are often not pure, but are either mixed together or contain additives, we first demonstrate that a MLIP can be trained to be compositionally transferable; i.e., the MLIP can be applied to mixtures of ions not directly trained on, while only being trained on a few mixtures of the same ions. We also investigated the accuracy of MLIPs for a novel IL, which we experimentally synthesize and characterize. Our MLIP trained on ∼200 DFT frames is in reasonable agreement with our experiments and DFT.
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Affiliation(s)
- Zachary A H Goodwin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Malia B Wenny
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Julia H Yang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
- Harvard University Center for the Environment, 26 Oxford St., Cambridge, Massachusetts 02138, United States
| | - Andrea Cepellotti
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Jingxuan Ding
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Kyle Bystrom
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Blake R Duschatko
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Anders Johansson
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Lixin Sun
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Simon Batzner
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Albert Musaelian
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Jarad A Mason
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Boris Kozinsky
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
- Research and Technology Center, Robert Bosch LLC, Cambridge, Massachusetts 02142, United States
| | - Nicola Molinari
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
- Research and Technology Center, Robert Bosch LLC, Cambridge, Massachusetts 02142, United States
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Liu J, Zhang X, Chen T, Zhang Y, Zhang D, Zhang L, Chen M. Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model. J Chem Theory Comput 2024; 20:5717-5731. [PMID: 38898771 DOI: 10.1021/acs.jctc.3c01320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal interatomic models that can be applied to a wide range of materials without tuning neural network parameters. We develop a unified deep-learning interatomic potential (the DPA-Semi model) for 19 semiconductors ranging from group IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS. In addition, independent deep potential models for each semiconductor are prepared for detailed comparison. The training data are obtained by performing density functional theory calculations with numerical atomic orbitals basis sets to reduce the computational costs. We systematically compare various properties of the solid and liquid phases of semiconductors between different machine-learning models. We conclude that the DPA-Semi model achieves GGA exchange-correlation functional quality accuracy and can be regarded as a pretrained model toward a universal model to study group IIB to VIA semiconductors.
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Affiliation(s)
- Jianchuan Liu
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P. R. China
| | - Xingchen Zhang
- College of Engineering, Peking University, Beijing 100871, P. R. China
| | - Tao Chen
- HEDPS, CAPT, School of Physics, Peking University, Beijing 100871, P. R. China
| | | | - Duo Zhang
- DP Technology, Beijing 100080, P. R. China
- Center for Machine Learning Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, P. R. China
- AI for Science Institute, Beijing 100080, P. R. China
| | - Linfeng Zhang
- DP Technology, Beijing 100080, P. R. China
- AI for Science Institute, Beijing 100080, P. R. China
| | - Mohan Chen
- College of Engineering, Peking University, Beijing 100871, P. R. China
- AI for Science Institute, Beijing 100080, P. R. China
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Dong W, Tian H, Zhang W, Zhou JJ, Pang X. Development of NaCl-MgCl 2-CaCl 2 Ternary Salt for High-Temperature Thermal Energy Storage Using Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2024; 16:530-539. [PMID: 38126774 DOI: 10.1021/acsami.3c13412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
NaCl-MgCl2-CaCl2 eutectic ternary chloride salts are potential heat transfer and storage materials for high-temperature thermal energy storage. In this study, first-principles molecular dynamics simulation results were used as a data set to develop an interatomic potential for ternary chloride salts using a neural network machine learning method. Deep potential molecular dynamics (DPMD) simulations were performed to predict the microstructure and thermophysical properties of the NaCl-MgCl2-CaCl2 ternary salt. This work reveals that DPMD simulations can accurately calculate the microstructure and thermophysical properties of ternary chloride salts. The association strength of chloride ions and cations follows the order of Mg2+ > Ca2+ > Na+, and the coordination number decreases gradually with increasing temperature, indicating a progressively looser and more disordered molten structure. Furthermore, thermophysical properties, such as density, specific heat capacity, thermal conductivity, and viscosity, are in good agreement with the experimental measurements. Machine learning molecular dynamics will provide a feasible multivariate molten salt exploration method for the design of next-generation solar power plants and thermal energy storage systems.
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Affiliation(s)
- Wenhao Dong
- School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Heqing Tian
- School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Wenguang Zhang
- School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Jun-Jie Zhou
- School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Xinchang Pang
- School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
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Xu T, Li X, Wang Y, Tang Z. Development of Deep Potentials of Molten MgCl 2-NaCl and MgCl 2-KCl Salts Driven by Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 36881968 DOI: 10.1021/acsami.2c19272] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Molten MgCl2-based chlorides have emerged as potential thermal storage and heat transfer materials due to high thermal stabilities and lower costs. In this work, deep potential molecular dynamics (DPMD) simulations by a method combination of the first principle, classical molecular dynamics, and machine learning are performed to systemically study the relationships of structures and thermophysical properties of molten MgCl2-NaCl (MN) and MgCl2-KCl (MK) eutectic salts at the temperature range of 800-1000 K. The densities, radial distribution functions, coordination numbers, potential mean forces, specific heat capacities, viscosities, and thermal conductivities of these two chlorides are successfully reproduced under extended temperatures by DPMD with a larger size (5.2 nm) and longer timescale (5 ns). It is concluded that the higher specific heat capacity of molten MK is originated from the strong potential mean force of Mg-Cl bonds, whereas the molten MN performs better in heat transfer due to the larger thermal conductivity and lower viscosity, attributed to the weak interaction between Mg and Cl ions. Innovatively, the plausibility and reliability of microscopic structures and macroscopic properties for molten MN and MK verify the extensibilities of these two deep potentials in temperatures, and these DPMD results also provide detailed technical parameters to the simulations of other formulated MN and MK salts.
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Affiliation(s)
- Tingrui Xu
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xuejiao Li
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Interfacial Physics and Technology, Chinese Academy of Sciences, Shanghai 201800, China
| | - Yang Wang
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
- Key Laboratory of Interfacial Physics and Technology, Chinese Academy of Sciences, Shanghai 201800, China
| | - Zhongfeng Tang
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Interfacial Physics and Technology, Chinese Academy of Sciences, Shanghai 201800, China
- Dalian National Laboratory for Clean Energy, Dalian 116023, China
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Attarian S, Morgan D, Szlufarska I. Thermophysical properties of FLiBe using moment tensor potentials. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Porter T, Vaka MM, Steenblik P, Della Corte D. Computational methods to simulate molten salt thermophysical properties. Commun Chem 2022; 5:69. [PMID: 36697757 PMCID: PMC9814384 DOI: 10.1038/s42004-022-00684-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 05/11/2022] [Indexed: 01/28/2023] Open
Abstract
Molten salts are important thermal conductors used in molten salt reactors and solar applications. To use molten salts safely, accurate knowledge of their thermophysical properties is necessary. However, it is experimentally challenging to measure these properties and a comprehensive evaluation of the full chemical space is unfeasible. Computational methods provide an alternative route to access these properties. Here, we summarize the developments in methods over the last 70 years and cluster them into three relevant eras. We review the main advances and limitations of each era and conclude with an optimistic perspective for the next decade, which will likely be dominated by emerging machine learning techniques. This article is aimed to help researchers in peripheral scientific domains understand the current challenges of molten salt simulation and identify opportunities to contribute.
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Affiliation(s)
- Talmage Porter
- grid.253294.b0000 0004 1936 9115Department of Physics and Astronomy, Brigham Young University, Provo, UT USA
| | - Michael M. Vaka
- grid.253294.b0000 0004 1936 9115Department of Physics and Astronomy, Brigham Young University, Provo, UT USA
| | - Parker Steenblik
- grid.253294.b0000 0004 1936 9115Department of Physics and Astronomy, Brigham Young University, Provo, UT USA
| | - Dennis Della Corte
- grid.253294.b0000 0004 1936 9115Department of Physics and Astronomy, Brigham Young University, Provo, UT USA
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Investigation on the local structure and properties of molten Li2CO3-K2CO3 binary salts by machine learning potentials. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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8
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Sun Y, Tan M, Li T, Li J, Shang B. Study on the structural properties of refining slags by molecular dynamics with deep learning potential. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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9
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Yang W, Li J, Chen X, Feng Y, Wu C, Gates ID, Gao Z, Ding X, Yao J, Li H. Exploring the Effects of Ionic Defects on the Stability of CsPbI 3 with a Deep Learning Potential. Chemphyschem 2022; 23:e202100841. [PMID: 35199438 DOI: 10.1002/cphc.202100841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/13/2022] [Indexed: 01/08/2023]
Abstract
Inorganic metal halide perovskites, such as CsPbI3 , have recently drawn extensive attention due to their excellent optical properties and high photoelectric efficiencies. However, the structural instability originating from inherent ionic defects leads to a sharp drop in the photoelectric efficiency, which significantly limits their applications in solar cells. The instability induced by ionic defects remains unresolved due to its complicated reaction process. Herein, to explore the effects of ionic defects on stability, we develop a deep learning potential for a CsPbI3 ternary system based upon density functional theory (DFT) calculated data for large-scale molecular dynamics (MD) simulations. By exploring 2.4 million configurations, of which 7,730 structures are used for the training set, the deep learning potential shows an accuracy approaching DFT-level. Furthermore, MD simulations with a 5,000-atom system and a one nanosecond timeframe are performed to explore the effects of bulk and surface defects on the stability of CsPbI3 . This deep learning potential based MD simulation provides solid evidence together with the derived radial distribution functions, simulated diffraction of X-rays, instability temperature, molecular trajectory, and coordination number for revealing the instability mechanism of CsPbI3 . Among bulk defects, Cs defects have the most significant influence on the stability of CsPbI3 with a defect tolerance concentration of 0.32 %, followed by Pb and I defects. With regards to surface defects, Cs defects have the largest impact on the stability of CsPbI3 when the defect concentration is less than 15 %, whereas Pb defects act play a dominant role for defect concentrations exceeding 20 %. Most importantly, this machine-learning-based MD simulation strategy provides a new avenue to explore the ionic defect effects on the stability of perovskite-like materials, laying a theoretical foundation for the design of stable perovskite materials.
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Affiliation(s)
- Weijie Yang
- Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, 071003, China
| | - Jiajia Li
- Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, 071003, China
| | - Xuelu Chen
- Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, 071003, China
| | - Yajun Feng
- Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, 071003, China
| | - Chongchong Wu
- Department of Chemical and Petroleum Engineering, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Ian D Gates
- Department of Chemical and Petroleum Engineering, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Zhengyang Gao
- Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, 071003, China
| | - Xunlei Ding
- School of Mathematics and Physics, North China Electric Power University, Beijing, 102206, China.,Institute of Clusters and Low Dimensional Nanomaterials, School of Mathematics and Physics, North China Electric Power University, Beijing, 102206, China
| | - Jianxi Yao
- State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China.,Beijing Key Laboratory of Energy Safety and Clean Utilization, North China Electric Power University, Beijing, 102206, China
| | - Hao Li
- Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan
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Roy S, Brehm M, Sharma S, Wu F, Maltsev DS, Halstenberg P, Gallington LC, Mahurin SM, Dai S, Ivanov AS, Margulis CJ, Bryantsev VS. Unraveling Local Structure of Molten Salts via X-ray Scattering, Raman Spectroscopy, and Ab Initio Molecular Dynamics. J Phys Chem B 2021; 125:5971-5982. [PMID: 34037400 DOI: 10.1021/acs.jpcb.1c03786] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In this work, we resolve a long-standing issue concerning the local structure of molten MgCl2 by employing a multimodal approach, including X-ray scattering and Raman spectroscopy, along with the theoretical modeling of the experimental spectra based on ab initio molecular dynamics (AIMD) simulations utilizing several density functional theory (DFT) methods. We demonstrate the reliability of AIMD simulations in achieving excellent agreement between the experimental and simulated spectra for MgCl2 and 50 mol % MgCl2 + 50 mol % KCl, and ZnCl2, thus allowing structural insights not directly available from experiment alone. A thorough computational analysis using five DFT methods provides a convergent view that octahedrally coordinated magnesium in pure MgCl2 upon melting preferentially coordinates with five chloride anions to form distorted square pyramidal polyhedra that are connected via corners and to a lesser degree via edges. This is contrasted with the results for ZnCl2, which does not change its tetrahedral coordination on melting. Although the five-coordinate MgCl53- complex was not considered in the early literature, together with an increasing tendency to form a tetrahedrally coordinated complex with decreasing the MgCl2 content in the mixture with alkali metal chloride systems, current work reconciles the results of most previous seemingly contradictory experimental studies.
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Affiliation(s)
- Santanu Roy
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Martin Brehm
- Institut für Chemie, Martin-Luther-Universität Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120 Halle (Saale), Germany
| | - Shobha Sharma
- Department of Chemistry, University of Iowa, Iowa City, Iowa 52242, United States
| | - Fei Wu
- Department of Chemistry, University of Iowa, Iowa City, Iowa 52242, United States
| | - Dmitry S Maltsev
- Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Phillip Halstenberg
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.,Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Leighanne C Gallington
- X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Argonne, Illinois 60439, United States
| | - Shannon M Mahurin
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Sheng Dai
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.,Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Alexander S Ivanov
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Claudio J Margulis
- Department of Chemistry, University of Iowa, Iowa City, Iowa 52242, United States
| | - Vyacheslav S Bryantsev
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
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Liang W, Lu G, Yu J. Machine-Learning-Driven Simulations on Microstructure and Thermophysical Properties of MgCl 2-KCl Eutectic. ACS APPLIED MATERIALS & INTERFACES 2021; 13:4034-4042. [PMID: 33430593 DOI: 10.1021/acsami.0c20665] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Theoretical studies on the MgCl2-KCl eutectic heavily rely on ab initio calculations based on density functional theory (DFT). However, neither large-scale nor long-time calculations are feasible in the framework of the ab initio method, which makes it challenging to accurately predict some properties. To address this issue, a scheme based on ab initio calculation, deep neural networks, and machine learning is introduced. By training on high-quality data sets generated by ab initio calculations, a deep potential (DP) is constructed to describe the interaction between atoms. This work shows that the DP enables higher efficiency and similar accuracy relative to DFT. By performing molecular dynamics simulations with DP, the microstructure and thermophysical properties of the MgCl2-KCl eutectic (32:68 mol %) are investigated. The structural evolution with temperature is analyzed through partial radial distribution functions, coordination numbers, angular distribution functions, and structural factors. Meanwhile, the estimated thermophysical properties are discussed, including density, thermal expansion coefficient, shear viscosity, self-diffusion coefficient, and specific heat capacity. It reveals that the Mg2+ ions in this system have a distorted tetrahedral geometry rather than an octahedral one (with vacancies). The microstructure of the MgCl2-KCl eutectic shows the feature of medium-range order, and this feature will be enhanced at a higher temperature. All predicted thermophysical properties are in good agreement with the experimental results. The hydrodynamic radius determined from the shear viscosity and self-diffusion coefficient shows that the Mg2+ ions have a strong local structure and diffuse as if with an intact coordination shell. Overall, this work provides a thorough understanding of the microstructure and enriches the data of the thermophysical properties of the MgCl2-KCl eutectic.
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Affiliation(s)
- Wenshuo Liang
- School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
- National Engineering Research Center for Integrated Utilization of Salt Lake Resource, East China University of Science and Technology, Shanghai 200237, China
| | - Guimin Lu
- School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
- National Engineering Research Center for Integrated Utilization of Salt Lake Resource, East China University of Science and Technology, Shanghai 200237, China
| | - Jianguo Yu
- School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
- National Engineering Research Center for Integrated Utilization of Salt Lake Resource, East China University of Science and Technology, Shanghai 200237, China
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