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Kondratyuk N, Ryltsev R, Ankudinov V, Chtchelkatchev N. First-principles calculations of the viscosity in multicomponent metallic melts: Al-Cu-Ni as a test case. J Mol Liq 2023. [DOI: 10.1016/j.molliq.2023.121751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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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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Tang L, Ho KM, Wang CZ. Molecular dynamics simulation of metallic Al-Ce liquids using a neural network machine learning interatomic potential. J Chem Phys 2021; 155:194503. [PMID: 34800941 DOI: 10.1063/5.0066061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Al-rich Al-Ce alloys have the possibility of replacing heavier steel and cast irons for use in high-temperature applications. Knowledge about the structures and properties of Al-Ce alloys at the liquid state is vital for optimizing the manufacture process to produce desired alloys. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. Here, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloys. Ab initio molecular dynamics simulation data on the Al-Ce liquid with a small unit cell (∼200 atoms) and on the known Al-Ce crystalline compounds are collected to train the interatomic potential using ANN-ML. The obtained ANN-ML model reproduces well the energies, forces, and atomic structure of the Al90Ce10 liquid and crystalline phases of Al-Ce compounds in comparison with the ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of the metallic Al90Ce10 liquid, which would provide useful insight into the guiding experimental process to produce desired Al-Ce alloys.
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Affiliation(s)
- L Tang
- Department of Applied Physics, College of Science, Zhejiang University of Technology, Hangzhou 310023, China
| | - K M Ho
- Ames Laboratory-USDOE, Iowa State University, Ames, Iowa 50011, USA
| | - C Z Wang
- Ames Laboratory-USDOE, Iowa State University, Ames, Iowa 50011, USA
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Jesus WS, Prudente FV, Marques JMC, Pereira FB. Modeling microsolvation clusters with electronic-structure calculations guided by analytical potentials and predictive machine learning techniques. Phys Chem Chem Phys 2021; 23:1738-1749. [PMID: 33427847 DOI: 10.1039/d0cp05200k] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
We propose a new methodology to study, at the density functional theory (DFT) level, the clusters resulting from the microsolvation of alkali-metal ions with rare-gas atoms. The workflow begins with a global optimization search to generate a pool of low-energy minimum structures for different cluster sizes. This is achieved by employing an analytical potential energy surface (PES) and an evolutionary algorithm (EA). The next main stage of the methodology is devoted to establish an adequate DFT approach to treat the microsolvation system, through a systematic benchmark study involving several combinations of functionals and basis sets, in order to characterize the global minimum structures of the smaller clusters. In the next stage, we apply machine learning (ML) classification algorithms to predict how the low-energy minima of the analytical PES map to the DFT ones. An early and accurate detection of likely DFT local minima is extremely important to guide the choice of the most promising low-energy minima of large clusters to be re-optimized at the DFT level of theory. In this work, the methodology was applied to the Li+Krn (n = 2-14 and 16) microsolvation clusters for which the most competitive DFT approach was found to be the B3LYP-D3/aug-pcseg-1. Additionally, the ML classifier was able to accurately predict most of the solutions to be re-optimized at the DFT level of theory, thereby greatly enhancing the efficiency of the process and allowing its applicability to larger clusters.
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Affiliation(s)
- W S Jesus
- Instituto de Física, Universidade Federal da Bahia, 40170-115 Salvador, BA, Brazil.
| | - F V Prudente
- Instituto de Física, Universidade Federal da Bahia, 40170-115 Salvador, BA, Brazil.
| | - J M C Marques
- CQC, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal.
| | - F B Pereira
- Coimbra Polytechnic - ISEC, Coimbra, Portugal and Centro de Informática e Sistemas da Universidade de Coimbra (CISUC), Coimbra, Portugal.
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Liang W, Lu G, Yu J. Molecular Dynamics Simulations of Molten Magnesium Chloride Using Machine‐Learning‐Based Deep Potential. ADVANCED THEORY AND SIMULATIONS 2020. [DOI: 10.1002/adts.202000180] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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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