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Zhang QM, Wang ZY, Zhang H, Liu XH, Zhang W, Zhao LB. Micro-kinetic modelling of the CO reduction reaction on single atom catalysts accelerated by machine learning. Phys Chem Chem Phys 2024; 26:11037-11047. [PMID: 38526740 DOI: 10.1039/d4cp00325j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Electrochemical CO2 transformation to fuels and chemicals is an effective strategy for conversion of renewable electric energy into storable chemical energy in combination with reducing green-house gas emission. Metal-nitrogen-carbon (M-N-C) single atom catalysts (SAC) have shown great potential in the electrochemical CO2 reduction reaction (CO2RR). However, exploring advanced SACs with simultaneously high catalytic activity and high product selectivity remains a great challenge. In this study, density functional theory (DFT) calculations are combined with machine learning (ML) for rapid and high-throughput screening of high performance CO reduction catalysts. Firstly, the electrochemical properties of 99 M-N-C SACs were calculated by DFT and used as a database. By using different machine learning models with simple features, the investigated SACs were expanded from 99 to 297. Through several effective indicators of catalyst stability, inhibition of the hydrogen evolution reaction, and CO adsorption strength, 33 SACs were finally selected. The catalytic activity and selectivity of the remaining 33 SACs were explored by micro-kinetic simulation based on Marcus theory. Among all the studied SACs, Mn-NC2, Pt-NC2, and Au-NC2 deliver the best catalytic performance and can be used as potential catalysts for CO2/CO conversion to hydrocarbons with high energy density. This effective screening method using a machine learning algorithm can promote the exploration of CO2RR catalysts and significantly reduce the simulation cost.
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Affiliation(s)
- Qing-Meng Zhang
- Department of Chemistry, School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China.
| | - Zhao-Yu Wang
- Department of Chemistry, School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China.
| | - Hao Zhang
- Department of Chemistry, School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China.
| | - Xiao-Hong Liu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China
- National University of Singapore (Chongqing) Research Institute, Chongqing 401123, China.
| | - Wei Zhang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China
| | - Liu-Bin Zhao
- Department of Chemistry, School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China.
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Li L, Liu H, Qin Y, Wang H, Han J, Zhu X, Ge Q. Coupled oxygen desorption and structural reconstruction accompanying reduction of copper oxide. J Chem Phys 2023; 158:054702. [PMID: 36754813 DOI: 10.1063/5.0136537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Understanding structural transformation and phase transition accompanying reactions in a solid as a catalyst or oxygen carrier is important to the design and optimization of many catalytic or chemical looping reaction processes. Herein, we combined density functional theory calculation with the stochastic surface walking global optimization approach to track the structural transformation accompanying the reduction of CuO upon releasing oxygen. We then used machine learning (ML) methods to correlate the structural properties of CuOx with varying x. By decomposing a reduction step into oxygen detachment and structural reconstruction, we identified two types of pathways: (1) uniform reduction with minimal structural changes; (2) segregated reduction with significant reconstruction. The results of ML analysis showed that the most important feature is the radial distribution functions of Cu-O at a percentage of oxygen vacancy [C(OV)] < 50% and Cu-Cu at C(OV) > 50% for CuOx formation. These features reflect the underlying physicochemical origin, i.e., Cu-O breaking and Cu-Cu formation in the respective stage of reduction. Phase diagram analysis indicates that CuO will be reduced to Cu2O under a typical oxygen uncoupling condition. This work demonstrates the complexity of solid structural transformation and the potential of ML methods in studying solid state materials involved in many chemical processes.
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Affiliation(s)
- Liwen Li
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Huixian Liu
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Yuyao Qin
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Hua Wang
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Jinyu Han
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Xinli Zhu
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Qingfeng Ge
- Department of Chemistry and Biochemistry, Southern Illinois University, Carbondale, Illinois 62901, USA
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Yao Y. Theoretical methods for structural phase transitions in elemental solids at extreme conditions: statics and dynamics. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2022; 34:363001. [PMID: 35724660 DOI: 10.1088/1361-648x/ac7a82] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
In recent years, theoretical studies have moved from a traditionally supporting role to a more proactive role in the research of phase transitions at high pressures. In many cases, theoretical prediction leads the experimental exploration. This is largely owing to the rapid progress of computer power and theoretical methods, particularly the structure prediction methods tailored for high-pressure applications. This review introduces commonly used structure searching techniques based on static and dynamic approaches, their applicability in studying phase transitions at high pressure, and new developments made toward predicting complex crystalline phases. Successful landmark studies for each method are discussed, with an emphasis on elemental solids and their behaviors under high pressure. The review concludes with a perspective on outstanding challenges and opportunities in the field.
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Affiliation(s)
- Yansun Yao
- Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E2, Canada
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Yang L, Zhu L, Zhang S, Hong X. Machine Learning Prediction of
Structure‐Performance
Relationship in Organic Synthesis. CHINESE J CHEM 2022. [DOI: 10.1002/cjoc.202200039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Li‐Cheng Yang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University Hangzhou Zhejiang 310027 China
| | - Lu‐Jing Zhu
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University Hangzhou Zhejiang 310027 China
| | - Shuo‐Qing Zhang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University Hangzhou Zhejiang 310027 China
| | - Xin Hong
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University Hangzhou Zhejiang 310027 China
- Beijing National Laboratory for Molecular Sciences, Zhongguancun North First Street NO. 2 Beijing 100190 China
- Key Laboratory of Precise Synthesis of Functional Molecules of Zhejiang Province, School of Science, Westlake University, 18 Shilongshan Road Hangzhou Zhejiang 310024 China
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Jin L, Ji Y, Wang H, Ding L, Li Y. First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning. Phys Chem Chem Phys 2021; 23:21470-21483. [PMID: 34570138 DOI: 10.1039/d1cp02963k] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The challenge of regeneration of batteries requires a performance improvement in the alkali/alkaline metal ion battery (AMIB) materials, whereas the traditional research paradigm fully based on experiments and theoretical simulations needs massive research and development investment. During the last decade, machine learning (ML) has made breakthroughs in many complex disciplines, which testifies to their high processing speed and ability to capture relationships. Inspired by these achievements, ML has also been introduced to bring a new paradigm for shortening the development of AMIB materials. In this Perspective, the focus will be on how this new ML technology solves the key problems of redox potentials, ionic conductivity and stability parameters in first-principles materials' simulation and design for AMIBs. It is found that ML not only accelerates the property prediction, but also gives physicochemical insights into AMIB materials' design. In addition, the final part of this paper summarizes current achievements and looks forward to the progress of a novel paradigm in direct/inverse design with the increasing number of databases, skills, and ML technologies for AMIBs.
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Affiliation(s)
- Lujie Jin
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
| | - Yujin Ji
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
| | - Hongshuai Wang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
| | - Lifeng Ding
- Department of Chemistry, Xi'an JiaoTong-Liverpool University, 111 Ren'ai Road, Suzhou Dushu Lake, Higher Education Town, Jiangsu Province 215123, China
| | - Youyong Li
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China. .,Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, Macau SAR 999078, China
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