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Ding R, Chen J, Chen Y, Liu J, Bando Y, Wang X. Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation. Chem Soc Rev 2024. [PMID: 39382108 DOI: 10.1039/d4cs00844h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2024]
Abstract
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.
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Affiliation(s)
- Rui Ding
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
- Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL 60439, USA.
| | - Junhong Chen
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
- Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL 60439, USA.
| | - Yuxin Chen
- Department of Computer Science, University of Chicago, Chicago, IL 60637, USA.
| | - Jianguo Liu
- Institute of Energy Power Innovation, North China Electric Power University, Beijing, 102206, China
| | - Yoshio Bando
- Chemistry Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Xuebin Wang
- College of Engineering and Applied Sciences, Nanjing University, Nanjing, 210093, China.
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2
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Li J, Liu Z, Zhao Z, Wang D. A Connected Convolutional Neutral Network Protocol for Design of Two-Dimensional Materials Based on Modified Graphdiyne. J Phys Chem Lett 2024:7840-7849. [PMID: 39052764 DOI: 10.1021/acs.jpclett.4c01485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
In materials science, doping plays a crucial role in manipulating the electronic properties of materials. Conventional screening via a trial-and-error strategy is challenging owing to the enormous chemical space. We proposed a connected convolutional neutral network (CCNN) for quick screening of boron nitrogen (B-N) codoped graphdiyne in terms of band gap. A paired-atomic localized matrix (PALM) descriptor was designed to describe the local chemical environment of materials with the matrix form adapted to a neutral network. An attribution analysis was conducted, and a quantitative relationship between structure and band gap is proposed, which reveals more significant influence of B-N doping at sp2 hybridized sites than at sp hybridized sites on broadening of the band gap of GDY. The accuracy and efficiency of the proposed approach implicate its potential in promoting the design of graphdiyne-based optoelectronic devices and catalysts with expected electronic properties, opening a new avenue for rational design of novel materials.
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Affiliation(s)
- Junqing Li
- State Key Laboratory of Fine Chemicals, Liaoning Key Laboratory for Catalytic Conversion of Carbon Resources, School of Chemistry, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Ziyi Liu
- State Key Laboratory of Fine Chemicals, Liaoning Key Laboratory for Catalytic Conversion of Carbon Resources, School of Chemistry, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Zhehuan Zhao
- Dalian University of Technology, and Key Laboratory for Ubiquitous Network and Service Software of Liaoning, Dalian 116621, China
| | - Dongqi Wang
- State Key Laboratory of Fine Chemicals, Liaoning Key Laboratory for Catalytic Conversion of Carbon Resources, School of Chemistry, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
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3
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Fu Q, Xu T, He C, Wang D, Liu M, Liu C. Machine Learning-Assisted Study of REN xC 6-x-Doped Graphene as Potential Electrocatalysts for Oxygen Electrode Reactions. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2024; 40:10726-10736. [PMID: 38717961 DOI: 10.1021/acs.langmuir.4c00803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
In the application of renewable energy, the oxidation-reduction reaction (ORR) and oxygen evolution reaction (OER) are two crucial reactions. Single-atom catalysts (SACs) based on metal-doped graphene have been widely employed due to their high activity and high atom utilization efficiency. However, the catalytic activity is significantly influenced by different metals and local coordination, making it challenging to efficiently screen through either experimental or density functional theory (DFT) calculations. To address this issue, this study employed a combination of DFT calculations and machine learning (DFT-ML) to investigate rare earth-modified carbon-based (RENxC6-x) electrocatalysts. Based on computational data from 75 catalysts, we trained two ML models to capture the underlying patterns of physical properties and overpotential. Subsequently, the candidate catalysts were screened, leading to the discovery of four ORR catalysts, nine OER catalysts, and five bifunctional electrocatalysts, all of which were thoroughly validated for their stability. Lastly, by integrating the ML models with the SHAP analysis framework, we revealed the influence of atomic radius, Pauling electronegativity, and other features on the catalytic activity. Additionally, we analyzed the physicochemical properties of potential catalysts through DFT calculations. The revolutionary DFT-ML approach provides a crucial driving force for the design and synthesis of potential catalysts in subsequent studies.
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Affiliation(s)
- Qiming Fu
- School of Materials Science and Engineering, Faculty of Materials Metallurgy and Chemistry, Jiangxi University of Science and Technology, Ganzhou 341000, People's Republic of China
| | - Tao Xu
- School of Materials Science and Engineering, Faculty of Materials Metallurgy and Chemistry, Jiangxi University of Science and Technology, Ganzhou 341000, People's Republic of China
| | - Chenggong He
- School of Materials Science and Engineering, Faculty of Materials Metallurgy and Chemistry, Jiangxi University of Science and Technology, Ganzhou 341000, People's Republic of China
| | - Daomiao Wang
- School of Materials Science and Engineering, Faculty of Materials Metallurgy and Chemistry, Jiangxi University of Science and Technology, Ganzhou 341000, People's Republic of China
| | - Meiling Liu
- School of Materials Science and Engineering, Faculty of Materials Metallurgy and Chemistry, Jiangxi University of Science and Technology, Ganzhou 341000, People's Republic of China
| | - Chao Liu
- School of Materials Science and Engineering, Faculty of Materials Metallurgy and Chemistry, Jiangxi University of Science and Technology, Ganzhou 341000, People's Republic of China
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4
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Oreshonkov AS, Sukhanova EV, Popov ZI. Phonon dynamics in MoSi 2N 4: insights from DFT calculations. Phys Chem Chem Phys 2023; 25:29831-29841. [PMID: 37888343 DOI: 10.1039/d3cp02921b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
We have reported the density functional theory investigations on the monolayered, 2 layered and bulk MoSi2N4 in three structural modifications called α1 [Y.-L. Hong, et al., Chemical Vapor Deposition of Layered Two-Dimensional MoSi2N4 Materials, Science, 2020, 369(6504), 670-674, DOI: 10.1126/science.abb7023], α2 and α3 [Y. Yin, Q. Gong, M. Yi and W. Guo, Emerging Versatile Two-Dimensional MoSi2N4 Family, Adv. Funct. Mater., 2023, 2214050, DOI: 10.1002/adfm.202214050]. We showed that in the case of monolayers the difference in total energies is less than 0.1 eV between α1 and α3 phases, and less than 0.2 eV between α1 and α2 geometries. The most energetically favorable layer stacking for the bulk structures of each phase was investigated. All considered modifications are dynamically stable from a single layer to a bulk structure in energetically favorable stacking. Raman spectra for the monolayered, 2 layered and bulk structures were simulated and the vibrational analysis was performed. The main difference in the obtained spectra is associated with the position of the strongest band which depends on the Mo-N bond length. According to the obtained data, we can conclude that the Raman line at 348 cm-1 in the experimental spectra of MoSi2N4 can have more complex explanation than just Γ-point Raman-active vibration as was discussed before in [Y.-L. Hong, et al., Chemical Vapor Deposition of Layered Two-Dimensional MoSi2N4 Materials, Science, 2020, 369(6504), 670-674, DOI: 10.1126/science.abb7023].
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Affiliation(s)
- A S Oreshonkov
- Emanuel Institute of Biochemical Physics of Russian Academy of Sciences, Moscow 119334, Russia
- Laboratory of Molecular Spectroscopy, Kirensky Institute of Physics, Federal Research Center KSC SB RAS, Krasnoyarsk 660036, Russia.
- School of Engineering and Construction, Siberian Federal University, Krasnoyarsk 660041, Russia
| | - E V Sukhanova
- Emanuel Institute of Biochemical Physics of Russian Academy of Sciences, Moscow 119334, Russia
- Moscow Institute of Physics and Technology, Institutsky lane 9, Dolgoprudny, Moscow region, 141700, Russia
| | - Z I Popov
- Emanuel Institute of Biochemical Physics of Russian Academy of Sciences, Moscow 119334, Russia
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Boonpalit K, Kinchagawat J, Prommin C, Nutanong S, Namuangruk S. Efficient exploration of transition-metal decorated MXene for carbon monoxide sensing using integrated active learning and density functional theory. Phys Chem Chem Phys 2023; 25:28657-28668. [PMID: 37849315 DOI: 10.1039/d3cp03667g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
The urgent demand for chemical safety necessitates the real-time detection of carbon monoxide (CO), a highly toxic gas. MXene, a 2D material, has shown potential for gas sensing applications (e.g., NH3, NO, SO2, CO2) due to its high surface accessibility, electrical conductivity, stability, and flexibility in surface functionalization. However, the pristine MXene generally exhibits poor interaction with CO; still, transition metal decoration can strengthen the interaction between CO and MXene. This study presents a high-throughput screening of 450 combinations of transition-metal (TM) decorated MXene (TM@MXene) for CO sensing applications using an integrated active learning (AL) and density functional theory (DFT) screening pipeline. Our AL pipeline, adopting a crystal graph convolutional neural network (CGCNN) as a surrogate model, successfully accelerates the screening of CO sensor candidates with minimal computational resources. This study identifies Sc@Zr3C2O2 and Y@Zr3C2O2 as the optimal TM@MXene candidates with promising CO sensing performance regarding the screening criteria of recovery time, surface stability, charge transfer, and sensitivity to CO. The proposed AL framework can be extended for property finetuning in the combinatorial chemical space.
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Affiliation(s)
- Kajjana Boonpalit
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand
| | - Jiramet Kinchagawat
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand
| | - Chanatkran Prommin
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand
| | - Sarana Nutanong
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand
| | - Supawadee Namuangruk
- National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Klong Luang, Pathum Thani 12120, Thailand
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6
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Yang KR, Kyro GW, Batista VS. The landscape of computational approaches for artificial photosynthesis. NATURE COMPUTATIONAL SCIENCE 2023; 3:504-513. [PMID: 38177419 DOI: 10.1038/s43588-023-00450-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 04/11/2023] [Indexed: 01/06/2024]
Abstract
Artificial photosynthesis is an attractive strategy for converting solar energy into fuels, largely because the Earth receives enough solar energy in one hour to meet humanity's energy needs for an entire year. However, developing devices for artificial photosynthesis remains difficult and requires computational approaches to guide and assist the interpretation of experiments. In this Perspective, we discuss current and future computational approaches, as well as the challenges of designing and characterizing molecular assemblies that absorb solar light, transfer electrons between interfaces, and catalyze water-splitting and fuel-forming reactions.
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Affiliation(s)
- Ke R Yang
- Department of Chemistry, Yale University, New Haven, CT, USA
- Energy Sciences Institute, Yale University, West Haven, CT, USA
| | - Gregory W Kyro
- Department of Chemistry, Yale University, New Haven, CT, USA
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven, CT, USA.
- Energy Sciences Institute, Yale University, West Haven, CT, USA.
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7
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Li Y, Wang H, Li Y, Ye H, Zhang Y, Yin R, Jia H, Hou B, Wang C, Ding H, Bai X, Lu A. Electron transfer rules of minerals under pressure informed by machine learning. Nat Commun 2023; 14:1815. [PMID: 37002237 PMCID: PMC10066309 DOI: 10.1038/s41467-023-37384-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/15/2023] [Indexed: 04/03/2023] Open
Abstract
Electron transfer is the most elementary process in nature, but the existing electron transfer rules are seldom applied to high-pressure situations, such as in the deep Earth. Here we show a deep learning model to obtain the electronegativity of 96 elements under arbitrary pressure, and a regressed unified formula to quantify its relationship with pressure and electronic configuration. The relative work function of minerals is further predicted by electronegativity, presenting a decreasing trend with pressure because of pressure-induced electron delocalization. Using the work function as the case study of electronegativity, it reveals that the driving force behind directional electron transfer results from the enlarged work function difference between compounds with pressure. This well explains the deep high-conductivity anomalies, and helps discover the redox reactivity between widespread Fe(II)-bearing minerals and water during ongoing subduction. Our results give an insight into the fundamental physicochemical properties of elements and their compounds under pressure.
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Affiliation(s)
- Yanzhang Li
- Beijing Key Laboratory of Mineral Environmental Function, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
- Key Laboratory of Orogenic Belts and Crustal Evolution, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
| | - Hongyu Wang
- Image Processing Center, Beihang University, 102206, Beijing, China
| | - Yan Li
- Beijing Key Laboratory of Mineral Environmental Function, School of Earth and Space Sciences, Peking University, 100871, Beijing, China.
- Key Laboratory of Orogenic Belts and Crustal Evolution, School of Earth and Space Sciences, Peking University, 100871, Beijing, China.
| | - Huan Ye
- Beijing Key Laboratory of Mineral Environmental Function, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
- Key Laboratory of Orogenic Belts and Crustal Evolution, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
| | - Yanan Zhang
- Image Processing Center, Beihang University, 102206, Beijing, China
| | - Rongzhang Yin
- Beijing Key Laboratory of Mineral Environmental Function, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
- Key Laboratory of Orogenic Belts and Crustal Evolution, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
| | - Haoning Jia
- Beijing Key Laboratory of Mineral Environmental Function, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
- Key Laboratory of Orogenic Belts and Crustal Evolution, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
| | - Bingxu Hou
- Beijing Key Laboratory of Mineral Environmental Function, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
- Key Laboratory of Orogenic Belts and Crustal Evolution, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
| | - Changqiu Wang
- Beijing Key Laboratory of Mineral Environmental Function, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
- Key Laboratory of Orogenic Belts and Crustal Evolution, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
| | - Hongrui Ding
- Beijing Key Laboratory of Mineral Environmental Function, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
- Key Laboratory of Orogenic Belts and Crustal Evolution, School of Earth and Space Sciences, Peking University, 100871, Beijing, China
| | - Xiangzhi Bai
- Image Processing Center, Beihang University, 102206, Beijing, China.
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 100191, Beijing, China.
- Advanced Innovation Center for Biomedical Engineering, Beihang University, 100083, Beijing, China.
| | - Anhuai Lu
- Beijing Key Laboratory of Mineral Environmental Function, School of Earth and Space Sciences, Peking University, 100871, Beijing, China.
- Key Laboratory of Orogenic Belts and Crustal Evolution, School of Earth and Space Sciences, Peking University, 100871, Beijing, China.
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8
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Ding H, Shi Y, Li Z, Wang S, Liang Y, Feng H, Deng Y, Song X, Pu P, Zhang X. Active Learning Accelerating to Screen Dual-Metal-Site Catalysts for Electrochemical Carbon Dioxide Reduction Reaction. ACS APPLIED MATERIALS & INTERFACES 2023; 15:12986-12997. [PMID: 36853996 DOI: 10.1021/acsami.2c21332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Dual-metal-site catalysts (DMSCs) are increasingly important catalysts in the field of electrochemical carbon dioxide reduction reaction (CO2RR) in recent years. However, rapid screening of suitable metal combinations of DMSCs remains a huge challenge. Herein, we constructed an active learning (AL) framework to study CO2RR to HCOOH. This AL framework turned out a success in the accurate prediction of 282 DMSCs for CO2RR through interactive learning between users and machine learning (ML) models. Among the 42 DMSCs calculated in three iteration loops of AL, 29 DMSCs were obtained, where the screening success rate was as high as 70%. Furthermore, we found five experimentally unexplored DMSCs that exhibited better CO2RR activity and selectivity than pure Bi. Low prediction errors on other DMSCs show that the AL model possessed outstanding universality. The results prove the excellent potential of the AL method and provide guidance on the design of high-performance electrocatalysts for CO2RR.
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Affiliation(s)
- Hu Ding
- State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Yawen Shi
- State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Zeyang Li
- State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Si Wang
- State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Yujie Liang
- State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Haisong Feng
- State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Yuan Deng
- State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Xin Song
- State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Pengxin Pu
- State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Xin Zhang
- State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
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9
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Steinmann SN, Wang Q, Seh ZW. How machine learning can accelerate electrocatalysis discovery and optimization. MATERIALS HORIZONS 2023; 10:393-406. [PMID: 36541226 DOI: 10.1039/d2mh01279k] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Advances in machine learning (ML) provide the means to bypass bottlenecks in the discovery of new electrocatalysts using traditional approaches. In this review, we highlight the currently achieved work in ML-accelerated discovery and optimization of electrocatalysts via a tight collaboration between computational models and experiments. First, the applicability of available methods for constructing machine-learned potentials (MLPs), which provide accurate energies and forces for atomistic simulations, are discussed. Meanwhile, the current challenges for MLPs in the context of electrocatalysis are highlighted. Then, we review the recent progress in predicting catalytic activities using surrogate models, including microkinetic simulations and more global proxies thereof. Several typical applications of using ML to rationalize thermodynamic proxies and predict the adsorption and activation energies are also discussed. Next, recent developments of ML-assisted experiments for catalyst characterization, synthesis optimization and reaction condition optimization are illustrated. In particular, the applications in ML-enhanced spectra analysis and the use of ML to interpret experimental kinetic data are highlighted. Additionally, we also show how robotics are applied to high-throughput synthesis, characterization and testing of electrocatalysts to accelerate the materials exploration process and how this equipment can be assembled into self-driven laboratories.
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Affiliation(s)
| | - Qing Wang
- Univ Lyon, ENS de Lyon, CNRS, Laboratoire de Chimie UMR 5182, Lyon, France.
| | - Zhi Wei Seh
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis, 138634, Singapore.
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10
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Zhang W, Huang W, Tan J, Huang D, Ma J, Wu B. Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives. CHEMOSPHERE 2023; 311:137044. [PMID: 36330979 DOI: 10.1016/j.chemosphere.2022.137044] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Dawei Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Jun Ma
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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11
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Huang Q, Liu X, Zhang Z, Wang L, Xiao B, Ao Z. Dopant-vacancy activated tetragonal transition metal selenide for hydrogen evolution electrocatalysis. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2022.108046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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12
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Mai H, Le TC, Chen D, Winkler DA, Caruso RA. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery. Chem Rev 2022; 122:13478-13515. [PMID: 35862246 DOI: 10.1021/acs.chemrev.2c00061] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.
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Affiliation(s)
- Haoxin Mai
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Tu C Le
- School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Dehong Chen
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia.,Biochemistry and Chemistry, La Trobe University, Kingsbury Drive, Bundoora, Victoria 3042, Australia.,School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Rachel A Caruso
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
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Gao Y, Zhang S, Sun X, Zhao W, Zhuo H, Zhuang G, Wang S, Yao Z, Deng S, Zhong X, Wei Z, Wang JG. Computational screening of O-functional MXenes for electrocatalytic ammonia synthesis. CHINESE JOURNAL OF CATALYSIS 2022. [DOI: 10.1016/s1872-2067(21)64011-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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14
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Sun F, Tang Q, Jiang DE. Theoretical Advances in Understanding and Designing the Active Sites for Hydrogen Evolution Reaction. ACS Catal 2022. [DOI: 10.1021/acscatal.2c02081] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Fang Sun
- School of Chemistry and Chemical Engineering, Chongqing Key Laboratory of Theoretical and Computational Chemistry, Chongqing University, Chongqing 401331, China
| | - Qing Tang
- School of Chemistry and Chemical Engineering, Chongqing Key Laboratory of Theoretical and Computational Chemistry, Chongqing University, Chongqing 401331, China
| | - De-en Jiang
- Department of Chemistry, University of California, Riverside, California 92521, United States
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