Accelerated strategy for fast ion conductor materials screening and optimal doping scheme exploration
Fast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks. The process usually includes structural optimization, energy calculation, charge analysis and ionic migration performance estimation. The first one involves looking for the equilibrium ato...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Journal of Materiomics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352847822000296 |
| _version_ | 1827884173283557376 |
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| author | Yuqi Wang Siyuan Wu Wei Shao Xiaorui Sun Qiang Wang Ruijuan Xiao Hong Li |
| author_facet | Yuqi Wang Siyuan Wu Wei Shao Xiaorui Sun Qiang Wang Ruijuan Xiao Hong Li |
| author_sort | Yuqi Wang |
| collection | DOAJ |
| description | Fast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks. The process usually includes structural optimization, energy calculation, charge analysis and ionic migration performance estimation. The first one involves looking for the equilibrium atomic positions in huge amount of candidate compounds or derivative structures, and the computational cost is always high because of the task-intensive features. The last one relates to the kinetic problems, for which the time-consuming transition state theory and the molecular dynamics are the main simulation methods. In this work, two predictive models, ionic migration activation energy model and structural optimization model, are developed based on machine learning (ML) techniques to accelerate the process of estimating activation energy and relaxing the doped crystal structures, respectively. By training 3136 energy barrier data calculated by bond valence (BV) method, an ionic migration activation energy model (Ea model) with mean absolute error (MAE) of 0.26 eV on testing data set is obtained. We apply this model and filter LiBiOS as a promising fast Li+ conductor from 49 Li-containing hetero-anionic compounds. Although the model-predicted result shows relatively low energy barrier, further analysis indicates that the high carrier formation energy restricts the ionic transportability. Therefore, we substitute fractional Li+ with Mg2+ in LiBiOS to relieve the large difficulty of forming carriers in the structure. In order to fast explore the optimal doping scheme, we develop the structural optimization model (E-f model) containing the ML-based energy and force prediction to accelerate the structural optimization under various LiMg ratio and doping configurations. Decent doping scheme Li1-2xMgxBiOS (x = 0.1875) shows much better Li+ migration performance compared with LiBiOS without substitution. This method of screening fast ion conductor materials and finding optimal doping scheme will extremely accelerate materials explorations. |
| first_indexed | 2024-03-12T19:25:37Z |
| format | Article |
| id | doaj.art-541a36a6a9264bd0b5ae9d85e44d8695 |
| institution | Directory Open Access Journal |
| issn | 2352-8478 |
| language | English |
| last_indexed | 2024-03-12T19:25:37Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materiomics |
| spelling | doaj.art-541a36a6a9264bd0b5ae9d85e44d86952023-08-02T04:53:11ZengElsevierJournal of Materiomics2352-84782022-09-018510381047Accelerated strategy for fast ion conductor materials screening and optimal doping scheme explorationYuqi Wang0Siyuan Wu1Wei Shao2Xiaorui Sun3Qiang Wang4Ruijuan Xiao5Hong Li6Beijing Advanced Innovation Center for Materials Genome Engineering, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China; School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China; School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, ChinaSamsung Research China – Beijing (SRC-B), Beijing, 100102, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China; School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, ChinaSamsung Research China – Beijing (SRC-B), Beijing, 100102, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China; School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China; Corresponding author. Beijing Advanced Innovation Center for Materials Genome Engineering, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China.Beijing Advanced Innovation Center for Materials Genome Engineering, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China; School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, ChinaFast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks. The process usually includes structural optimization, energy calculation, charge analysis and ionic migration performance estimation. The first one involves looking for the equilibrium atomic positions in huge amount of candidate compounds or derivative structures, and the computational cost is always high because of the task-intensive features. The last one relates to the kinetic problems, for which the time-consuming transition state theory and the molecular dynamics are the main simulation methods. In this work, two predictive models, ionic migration activation energy model and structural optimization model, are developed based on machine learning (ML) techniques to accelerate the process of estimating activation energy and relaxing the doped crystal structures, respectively. By training 3136 energy barrier data calculated by bond valence (BV) method, an ionic migration activation energy model (Ea model) with mean absolute error (MAE) of 0.26 eV on testing data set is obtained. We apply this model and filter LiBiOS as a promising fast Li+ conductor from 49 Li-containing hetero-anionic compounds. Although the model-predicted result shows relatively low energy barrier, further analysis indicates that the high carrier formation energy restricts the ionic transportability. Therefore, we substitute fractional Li+ with Mg2+ in LiBiOS to relieve the large difficulty of forming carriers in the structure. In order to fast explore the optimal doping scheme, we develop the structural optimization model (E-f model) containing the ML-based energy and force prediction to accelerate the structural optimization under various LiMg ratio and doping configurations. Decent doping scheme Li1-2xMgxBiOS (x = 0.1875) shows much better Li+ migration performance compared with LiBiOS without substitution. This method of screening fast ion conductor materials and finding optimal doping scheme will extremely accelerate materials explorations.http://www.sciencedirect.com/science/article/pii/S2352847822000296Fast ion conductorOptimal doping schemeMachine learningHigh-throughput computation |
| spellingShingle | Yuqi Wang Siyuan Wu Wei Shao Xiaorui Sun Qiang Wang Ruijuan Xiao Hong Li Accelerated strategy for fast ion conductor materials screening and optimal doping scheme exploration Journal of Materiomics Fast ion conductor Optimal doping scheme Machine learning High-throughput computation |
| title | Accelerated strategy for fast ion conductor materials screening and optimal doping scheme exploration |
| title_full | Accelerated strategy for fast ion conductor materials screening and optimal doping scheme exploration |
| title_fullStr | Accelerated strategy for fast ion conductor materials screening and optimal doping scheme exploration |
| title_full_unstemmed | Accelerated strategy for fast ion conductor materials screening and optimal doping scheme exploration |
| title_short | Accelerated strategy for fast ion conductor materials screening and optimal doping scheme exploration |
| title_sort | accelerated strategy for fast ion conductor materials screening and optimal doping scheme exploration |
| topic | Fast ion conductor Optimal doping scheme Machine learning High-throughput computation |
| url | http://www.sciencedirect.com/science/article/pii/S2352847822000296 |
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