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Ji S, Zhu J, Yang Y, Dos Reis G, Zhang Z. Data-Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects. SMALL METHODS 2024; 8:e2301021. [PMID: 38213008 DOI: 10.1002/smtd.202301021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/11/2023] [Indexed: 01/13/2024]
Abstract
Battery characterization and prognosis are essential for analyzing underlying electrochemical mechanisms and ensuring safe operation, especially with the assistance of superior data-driven artificial intelligence systems. This review provides a unique perspective on recent progress in data-driven battery characterization and prognosis methods. First, recent informative image characterization and impedance spectrum as well as high-throughput screening approaches on revealing battery electrochemical mechanisms at multiple scales are summarized. Thereafter, battery prognosis tasks and strategies are described, with the comparison of various physics-informed modeling strategies. Considering unlocking mechanisms from tremendous battery data, the dominant role of physics-informed interpretable learning in accelerating energy device development is presented. Finally, challenges and prospects on data-driven characterization and prognosis are discussed toward accelerating energy device development with much-enhanced electrochemical transparency and generalization. This review is hoped to supply new ideas and inspirations to the next-generation battery development.
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Affiliation(s)
- Shanling Ji
- School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China
| | - Jianxiong Zhu
- School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Yaxin Yang
- School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China
| | - Gonçalo Dos Reis
- School of Mathematics, University of Edinburgh, JCMB, Peter Guthrie Tait Road, Edinburgh, EH9 3FD, UK
| | - Zhisheng Zhang
- School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China
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Deng C, Li Y, Huang J. Building Smarter Aqueous Batteries. SMALL METHODS 2024; 8:e2300832. [PMID: 37670546 DOI: 10.1002/smtd.202300832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/23/2023] [Indexed: 09/07/2023]
Abstract
Amidst the global trend of advancing renewable energies toward carbon neutrality, energy storage becomes increasingly critical due to the intermittency of renewables. As an alternative to lithium-ion batteries (LIBs), aqueous batteries have received growing attention for large-scale energy storage due to their economical and safe features. Despite the fruitful achievements at the material level, the reliability and lifetime of aqueous batteries are still far from satisfactory. Alike LIBs, integrating smartness is essential for more reliable and long-life aqueous batteries via operando monitoring and automatic response to extreme abuses. In this review, recent advances in sensing techniques and multifunctional battery-sensor systems together with self-healing methods in aqueous batteries is summarized. The significant role of artificial intelligence in designing and optimizing aqueous batteries with high efficiency is also highlighted. Ultimately, it is extrapolated toward the future and present the humble perspective for building smarter aqueous batteries.
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Affiliation(s)
- Canbin Deng
- The Hong Kong University of Science and Technology (Guangzhou), Sustainable Energy and Environment Thrust and Guangzhou Municipal Key Laboratory of Materials Informatics, Nansha, Guangzhou, Guangdong, 511400, P. R. China
- Academy of Interdisciplinary Studies, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, 999077, P. R. China
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, Guangdong, 518045, P. R. China
| | - Yiqing Li
- The Hong Kong University of Science and Technology (Guangzhou), Sustainable Energy and Environment Thrust, Nansha, Guangzhou, Guangdong, 511400, P. R. China
| | - Jiaqiang Huang
- The Hong Kong University of Science and Technology (Guangzhou), Sustainable Energy and Environment Thrust and Guangzhou Municipal Key Laboratory of Materials Informatics, Nansha, Guangzhou, Guangdong, 511400, P. R. China
- Academy of Interdisciplinary Studies, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, 999077, P. R. China
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, Guangdong, 518045, P. R. China
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3
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Jia S, Chen Z, Li Y, Li C, Duan C, Lim KH, Kawi S. Construction of greenly biodegradable bacterial cellulose/UiO-66-NH 2 composite separators for efficient enhancing performance of lithium-ion battery. Int J Biol Macromol 2024; 269:131988. [PMID: 38701999 DOI: 10.1016/j.ijbiomac.2024.131988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/04/2024] [Accepted: 04/28/2024] [Indexed: 05/06/2024]
Abstract
The disposal of waste lithium batteries, especially waste separators, has always been a problem, incineration and burial will cause environmental pollution, therefore, the development of degradable and high-performance separators has become an important challenge. Herein, UiO-66-NH2 particles were successfully anchored onto bacterial cellulose (BC) separators by epichlorohydrin (ECH) as a crosslinker, then a BC/UiO-66-NH2 composite separator was prepared by vacuum filtration. The ammonia groups (-NH2) from UiO-66-NH2 can form hydrogen bonds with PF6- in the electrolyte, promoting lithium-ion transference. Additionally, UiO-66-NH2 can store the electrolyte and tune the porosity of the separator. The lithium ion migration number (0.62) of the battery assembled with BC/UiO-66-NH2 composite separator increased by 50 % compared to the battery assembled with commercial PP separator (0.45). The discharge specific capacity of the battery assembled with BC/UIO-66-NH2 composite separator after 50 charge and discharge cycles is 145.4 mAh/g, which is higher than the average discharge specific capacity of 114.3 mAh/g of the battery assembled with PP separator. When the current density is 2C, the minimum discharge capacity of the battery assembled with BC/UiO-66-NH2 composite separator is 85.3 mAh/g. The electrochemical performance of the BC/UiO-66-NH2 composite separator is significantly better than that of the commercial PP separator. In addition, -NH2 can offer a nitrogen source to facilitate degradation of the BC separators, whereby the BC/UiO-66-NH2 composite separator could be completely degraded in 15 days.
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Affiliation(s)
- Shuaitian Jia
- School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin, 300400, PR China
| | - Zan Chen
- Key Laboratory of Separator and Separator Process, China National Offshore Oil Corporation Tianjin Chemical Research & Design Institute, Tianjin, 300131, PR China.
| | - Yinhui Li
- School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin, 300400, PR China; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore.
| | - Claudia Li
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore
| | - Cuijia Duan
- Key Laboratory of Separator and Separator Process, China National Offshore Oil Corporation Tianjin Chemical Research & Design Institute, Tianjin, 300131, PR China
| | - Kang Hui Lim
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore
| | - Sibudjing Kawi
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore.
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4
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Wang X, Sheng Y, Ning J, Xi J, Xi L, Qiu D, Yang J, Ke X. A Critical Review of Machine Learning Techniques on Thermoelectric Materials. J Phys Chem Lett 2023; 14:1808-1822. [PMID: 36763950 DOI: 10.1021/acs.jpclett.2c03073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Thermoelectric (TE) materials can directly convert heat to electricity and vice versa and have broad application potential for solid-state power generation and refrigeration. Over the past few decades, efforts have been made to develop new TE materials with high performance. However, traditional experiments and simulations are expensive and time-consuming, limiting the development of new materials. Machine learning (ML) has been increasingly applied to study TE materials in recent years. This paper reviews the recent progress in ML-based TE material research. The application of ML in predicting and optimizing the properties of TE materials, including electrical and thermal transport properties and optimization of functional materials with targeted TE properties, is reviewed. Finally, future research directions are discussed.
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Affiliation(s)
- Xiangdong Wang
- Materials Genome Institute, Shanghai University, Shanghai200444, China
- School of Physics and Electronic Science, East China Normal University, Shanghai200241, China
| | - Ye Sheng
- Materials Genome Institute, Shanghai University, Shanghai200444, China
| | - Jinyan Ning
- Materials Genome Institute, Shanghai University, Shanghai200444, China
| | - Jinyang Xi
- Materials Genome Institute, Shanghai University, Shanghai200444, China
- Zhejiang Laboratory, Hangzhou, Zhejiang311100, China
| | - Lili Xi
- Materials Genome Institute, Shanghai University, Shanghai200444, China
- Zhejiang Laboratory, Hangzhou, Zhejiang311100, China
| | - Di Qiu
- Materials Genome Institute, Shanghai University, Shanghai200444, China
- Zhejiang Laboratory, Hangzhou, Zhejiang311100, China
| | - Jiong Yang
- Materials Genome Institute, Shanghai University, Shanghai200444, China
- Zhejiang Laboratory, Hangzhou, Zhejiang311100, China
| | - Xuezhi Ke
- School of Physics and Electronic Science, East China Normal University, Shanghai200241, China
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Bradford G, Lopez J, Ruza J, Stolberg MA, Osterude R, Johnson JA, Gomez-Bombarelli R, Shao-Horn Y. Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery. ACS CENTRAL SCIENCE 2023; 9:206-216. [PMID: 36844492 PMCID: PMC9951296 DOI: 10.1021/acscentsci.2c01123] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Indexed: 06/18/2023]
Abstract
Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by enhancing safety and enabling higher energy densities. However, SPEs suffer from significantly lower ionic conductivity than liquid and solid ceramic electrolytes, limiting their adoption in functional batteries. To facilitate more rapid discovery of high ionic conductivity SPEs, we developed a chemistry-informed machine learning model that accurately predicts ionic conductivity of SPEs. The model was trained on SPE ionic conductivity data from hundreds of experimental publications. Our chemistry-informed model encodes the Arrhenius equation, which describes temperature activated processes, into the readout layer of a state-of-the-art message passing neural network and has significantly improved accuracy over models that do not encode temperature dependence. Chemically informed readout layers are compatible with deep learning for other property prediction tasks and are especially useful where limited training data are available. Using the trained model, ionic conductivity values were predicted for several thousand candidate SPE formulations, allowing us to identify promising candidate SPEs. We also generated predictions for several different anions in poly(ethylene oxide) and poly(trimethylene carbonate), demonstrating the utility of our model in identifying descriptors for SPE ionic conductivity.
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Affiliation(s)
- Gabriel Bradford
- Department
of Mechanical Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Jeffrey Lopez
- Research
Laboratory of Electronics, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Jurgis Ruza
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Michael A. Stolberg
- Department
of Chemistry, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Richard Osterude
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Jeremiah A. Johnson
- Department
of Chemistry, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Rafael Gomez-Bombarelli
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Yang Shao-Horn
- Department
of Mechanical Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
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6
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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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