数据驱动的机器学习在电化学储能材料研究中的应用

doi:10.19799/j.cnki.2095-4239.2022.0051

储能科学与技术 ›› 2022, Vol. 11 ›› Issue (3): 739-759.doi: 10.19799/j.cnki.2095-4239.2022.0051

• 储能新材料设计与先进表征专刊 • 上一篇下一篇

数据驱动的机器学习在电化学储能材料研究中的应用

施思齐^1,^2,⁵(), 涂章伟¹, 邹欣欣³, 孙拾雨², 杨正伟³, 刘悦^3,⁴()

^1.上海大学材料科学与工程学院
^2.上海大学材料基因组工程研究院
^3.上海大学计算机工程与科学学院
^4.上海市智能计算系统工程技术研究中心，上海 200444
^5.之江实验室，浙江杭州 311100

收稿日期:2022-01-31 修回日期:2022-02-10 出版日期:2022-03-05 发布日期:2022-03-11
通讯作者: 刘悦 E-mail:sqshi@shu.edu.cn;yueliu@shu.edu.cn
作者简介:施思齐（1978—），男，教授，研究方向为电化学储能材料基础科学问题解析、计算方法发展和新材料设计，E-mail：sqshi@shu.edu.cn；
基金资助:
国家重点研发计划项目(2021YFB3802100);国家自然科学基金面上项目(52073169);之江实验室科研攻关项目(2021PE0AC02)

Applying data-driven machine learning to studying electrochemical energy storage materials

Siqi SHI^1,^2,⁵(), Zhangwei TU¹, Xinxin ZOU³, Shiyu SUN², Zhengwei YANG³, Yue LIU^3,⁴()

^1.School of Materials Science and Engineering, Shanghai University
^2.Materials Genome Institute, Shanghai University
^3.School of Computer Engineering and Science, Shanghai University
^4.Shanghai Engineering Research Center of Intelligent Computing System, Shanghai, 200444, China
^5.Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China

Received:2022-01-31 Revised:2022-02-10 Online:2022-03-05 Published:2022-03-11
Contact: Yue LIU E-mail:sqshi@shu.edu.cn;yueliu@shu.edu.cn

摘要/Abstract

摘要：

储能电池的关键是材料。继实验观测、理论研究和计算模拟之后，数据驱动的机器学习具有快速捕捉材料成分-结构-工艺-性能间复杂构效关系的优势，有望为电化学储能材料的研发提供新的范式。本文从结构化和非结构化数据驱动两方面，系统评述了机器学习在电化学储能材料研究中的最新进展。全面概括了可用于电化学储能材料机器学习的国内外材料数据库，分析了其数据的收集、共享和质量检测存在的问题；重点阐述了电化学储能材料中机器学习的工作流程和应用，包括结构化数据驱动下数据收集、特征工程和机器学习建模以及图形、表征图像和文献文本这类非结构化数据驱动下的模型构建和应用。进一步，厘清电化学储能材料领域机器学习面临的三大矛盾且给出对策，即高维度与小样本数据的矛盾与协调、模型复杂性与易用性的矛盾与统一、模型学习结果与专家经验的矛盾与融合，并提出构建“领域知识嵌入的机器学习方法”有望调和这些矛盾。本文将为机器学习在电化学储能材料设计和性能优化中的应用提供参考。

关键词: 电化学储能材料, 机器学习, 材料数据库, 领域知识

Abstract:

Materials are key to energy storage batteries. With experimental observations, theoretical research, and computational simulations, data-driven machine learning should provide a new paradigm for electrochemical energy storage material research and development. Its advantages include rapid capture of the complex structure-activity relationship between material composition, structure, process, and performance. In this study, the latest developments in employing machine learning in electrochemical energy storage materials are reviewed systematically from structured and unstructured data-driven perspectives. The material databases from China and abroad are summarized for electrochemical energy storage material use, and data collection and quality inspection problems are analyzed. Data-driven machine learning workflows and applications in electrochemical energy storage materials are demonstrated. They contain data collection, feature engineering, and machine learning modeling under structured data, and the model construction and application under unstructured data of graphics, representation images, and literature. Three principal contradictions and countermeasures faced by machine learning in electrochemical energy storage materials are highlighted: the contradiction and coordination of high-dimensional and small sample data, the contradiction and unity of model complexity and ease of use, and the contradiction and contradiction fusion between model learning results and expert experience. We highlighted that "Machine Learning Embedded with Domain Knowledge" construction should reconcile these contradictions. This review provides a reference for applying machine learning in electrochemical energy storage materials' design and performance optimization.

Key words: electrochemical energy storage materials, machine learning, materials database, domain knowledge

中图分类号:

TP 181

施思齐, 涂章伟, 邹欣欣, 孙拾雨, 杨正伟, 刘悦. 数据驱动的机器学习在电化学储能材料研究中的应用[J]. 储能科学与技术, 2022, 11(3): 739-759.

Siqi SHI, Zhangwei TU, Xinxin ZOU, Shiyu SUN, Zhengwei YANG, Yue LIU. Applying data-driven machine learning to studying electrochemical energy storage materials[J]. Energy Storage Science and Technology, 2022, 11(3): 739-759.

图/表 2

参考文献 147

1	张舒, 王少飞, 凌仕刚, 等. 锂离子电池基础科学问题(X)——全固态锂离子电池[J]. 储能科学与技术, 2014, 3(4): 376-394.
	ZHANG S, WANG S F, LING S G, et al. Fundamental scientific aspects of lithium ion batteries(X)—All-solid-state lithium-ion batteries[J]. Energy Storage Science and Technology, 2014, 3(4): 376-394.
2	李泓. 锂离子电池基础科学问题(XV)——总结和展望[J]. 储能科学与技术, 2015, 4(3): 306-318.
	LI H. Fundamental scientific aspects of lithium ion batteries(XV)—Summary and outlook[J]. Energy Storage Science and Technology, 2015, 4(3): 306-318.
3	任元, 邹喆乂, 赵倩, 等. 浅析电解质中离子输运的微观物理图像[J]. 物理学报, 2020, 69(22): 46-62.
	REN Y, ZOU Z Y, ZHAO Q, et al. Brief overview of microscopic physical image of ion transport in electrolytes[J]. Acta Physica Sinica, 2020, 69(22): 46-62.
4	郑杰允, 李泓. 锂电池基础科学问题(Ⅴ)——电池界面[J]. 储能科学与技术, 2013, 2(5): 503-513.
	ZHENG J Y, LI H. Fundamental scientific aspects of lithium batteries (Ⅴ)—Interfaces[J]. Energy Storage Science and Technology, 2013, 2(5): 503-513.
5	彭佳悦, 祖晨曦, 李泓. 锂电池基础科学问题(Ⅰ)——化学储能电池理论能量密度的估算[J]. 储能科学与技术, 2013, 2(1): 55-62.
	PENG J Y, ZU C X, LI H. Fundamental scientific aspects of lithium batteries(Ⅰ)—Thermodynamic calculations of theoretical energy densities of chemical energy storage systems[J]. Energy Storage Science and Technology, 2013, 2(1): 55-62.
6	施思齐, 徐积维, 崔艳华, 等. 多尺度材料计算方法[J]. 科技导报, 2015, 33(10): 20-30.
	SHI S Q, XU J W, CUI Y H, et al. Multiscale materials computational methods[J]. Science & Technology Review, 2015, 33(10): 20-30.
7	SHI S Q, GAO J, LIU Y, et al. Multi-scale computation methods: Their applications in lithium-ion battery research and development[J]. Chinese Physics B, 2016, 25(1): doi: 10.1088/1674-1056/25/1/018212.
8	FRANCO A A, RUCCI A, BRANDELL D, et al. Boosting rechargeable batteries R&D by multiscale modeling: Myth or reality?[J]. Chemical Reviews, 2019, 119(7): 4569-4627.
9	AGRAWAL A, CHOUDHARY A. Perspective: Materials informatics and big data: Realization of the "fourth paradigm" of science in materials science[J]. APL Materials, 2016, 4(5): doi: 10.1063/1.4946894.
10	BELL G, HEY T, SZALAY A. Beyond the data deluge[J]. Science, 2009, 323(5919): 1297-1298.
11	谢建新, 宿彦京, 薛德祯, 等. 机器学习在材料研发中的应用[J]. 金属学报, 2021, 57(11): 1343-1361.
	XIE J X, SU Y J, XUE D Z, et al. Machine learning for materials research and development[J]. Acta Metallurgica Sinica, 2021, 57(11): 1343-1361.
12	BUTLER K T, DAVIES D W, CARTWRIGHT H, et al. Machine learning for molecular and materials science[J]. Nature, 2018, 559(7715): 547-555.
13	WANG H S, JI Y J, LI Y Y. Simulation and design of energy materials accelerated by machine learning[J]. WIREs Computational Molecular Science, 2020, 10(1): doi: 10.1002/wcms.1421.
14	GAO T H, LU W. Machine learning toward advanced energy storage devices and systems[J]. iScience, 2021, 24(1): doi: 10.1016/j.isci.2020.101936.
15	LYU C, ZHOU X, ZHONG L, et al. Machine learning: An advanced platform for materials development and state prediction in lithium-ion batteries[J]. Advanced Materials, 2021, doi: 10.1002/adma.202101474.
16	LIU Y, ZHAO T L, JU W W, et al. Materials discovery and design using machine learning[J]. Journal of Materiomics, 2017, 3(3): 159-177.
17	LIU Y, WU J M, YANG G, et al. Predicting the onset temperature (Tg) of GexSe_1- x glass transition: A feature selection based two-stage support vector regression method[J]. Science Bulletin, 2019, 64(16): 1195-1203.
18	LIU Y, ZHAO T L, YANG G, et al. The onset temperature (Tg) of As_xSe_1- x glasses transition prediction: A comparison of topological and regression analysis methods[J]. Computational Materials Science, 2017, 140: 315-321.
19	SALKIND A J, FENNIE C, SINGH P, et al. Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology[J]. Journal of Power Sources, 1999, 80(1/2): 293-300.
20	MORGAN D, CEDER G, CURTAROLO S. Data mining approach to ab-initio prediction of crystal structure[J]. MRS Proceedings, 2003, 804: 305-310.
21	CURTAROLO S, MORGAN D, PERSSON K, et al. Predicting crystal structures with data mining of quantum calculations[J]. Physical Review Letters, 2003, 91(13): doi: 10.1103/PhysRevLett. 91.135503.
22	MORGAN D, CEDER G, CURTAROLO S. High-throughput and data mining with ab initio methods[J]. Measurement Science and Technology, 2005, 16(1): 296-301.
23	The materials genome initiative at the national science foundation: A status report after the first year of funded research[J]. JOM, 2014, 66(3): 336-344.
24	WIDENER A. Materials genome initiative[J]. Chemical & Engineering News Archive, 2013, 91(31): 25-27.
25	汪洪, 项晓东, 张澜庭. 数据+人工智能是材料基因工程的核心[J]. 科技导报, 2018, 36(14): 15-21.
	WANG H, XIANG X D, ZHANG L T. Data+AI: The core of materials genomic engineering[J]. Science & Technology Review, 2018, 36(14): 15-21.
26	GUO H Y, WANG Q, STUKE A, et al. Accelerated atomistic modeling of solid-state battery materials with machine learning[J]. Frontiers in Energy Research, 2021, 9: doi: 10.3389/fenrg.2021.695902.
27	CHEN X, LIU X Y, SHEN X, et al. Applying machine learning to rechargeable batteries: From the microscale to the macroscale[J]. Angewandte Chemie, 2021, 60(46): 24354-24366.
28	LOMBARDO T, DUQUESNOY M, EL-BOUYSIDY H, et al. Artificial intelligence applied to battery research: Hype or reality?[J]. Chemical Reviews, 2021, doi: 10.1021/acs.chemrev.1c00108.
29	LIU Y, GUO B R, ZOU X X, et al. Machine learning assisted materials design and discovery for rechargeable batteries[J]. Energy Storage Materials, 2020, 31: 434-450.
30	吴思远, 王宇琦, 肖睿娟, 等. 电池材料数据库的发展与应用[J]. 物理学报, 2020, 69(22): 9-16.
	WU S Y, WANG Y Q, XIAO R J, et al. Development and application of battery materials database[J]. Acta Physica Sinica, 2020, 69(22): 9-16.
31	ALLEN F H. The cambridge structural database: A quarter of a million crystal structures and rising[J]. Acta Crystallographica Section B, Structural Science, 2002, 58(Pt 3 Pt 1): 380-388.
32	GROOM C R, ALLEN F H. The cambridge structural database in retrospect and prospect[J]. Angewandte Chemie, 2014, 53(3): 662-671.
33	KENNARD O, ALLEN F, BELLARD S, et al. Current developments in the cambridge structural database[J]. Acta Crystallographica Section A Foundations of Crystallography, 1984, 40: C445.
34	BERGERHOFF G, HUNDT R, SIEVERS R, et al. The inorganic crystal structure data base[J]. Journal of Chemical Information and Computer Sciences, 1983, 23(2): 66-69.
35	BELSKY A, HELLENBRANDT M, KAREN V L, et al. New developments in the inorganic crystal structure database (ICSD): Accessibility in support of materials research and design[J]. Acta Crystallographica Section B, Structural Science, 2002, 58(Pt 3 Pt 1): 364-369.
36	VILLARS P, BERNDT M, BRANDENBURG K, et al. The Pauling file, binaries edition[J]. Journal of Alloys and Compounds, 2004, 367(1/2): 293-297.
37	JAIN A, ONG S P, HAUTIER G, et al. Commentary: The materials project: A materials genome approach to accelerating materials innovation[J]. APL Materials, 2013, 1(1): doi: 10.1063/1.4812323.
38	CURTAROLO S, SETYAWAN W, WANG S D, et al. AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations[J]. Computational Materials Science, 2012, 58: 227-235.
39	SAAL J E, KIRKLIN S, AYKOL M, et al. Materials design and discovery with high-throughput density functional theory: The open quantum materials database (OQMD)[J]. JOM, 2013, 65(11): 1501-1509.
40	NOSENGO N, CEDER G. Can artificial intelligence create the next wonder material?[J]. Nature, 2016, 533(7601): 22-25.
41	HE B, YE A, CHI S, et al. CAVD, towards better characterization of void space for ionic transport analysis[J]. Scientific Data, 2020, 7: doi: 10.1038/s41597-020-0491-x.
42	HE B, MI P H, YE A J, et al. A highly efficient and informative method to identify ion transport networks in fast ion conductors[J]. Acta Materialia, 2021, 203: doi: 10.1016/j.actamat.2020.116490.
43	ZHANG L W, HE B, ZHAO Q, et al. A database of ionic transport characteristics for over 29 000 inorganic compounds[J]. Advanced Functional Materials, 2020, 30(35): doi: 10.1002/adfm. 202003087.
44	RACCUGLIA P, ELBERT K C, ADLER P D, et al. Machine-learning-assisted materials discovery using failed experiments[J]. Nature, 2016, 533(7601): 73-76.
45	HIMANEN L, GEURTS A, FOSTER A S, et al. Data-driven materials science: Status, challenges, and perspectives[J]. Advanced Science, 2019, 6(21): doi: 10.1002/advs.201900808.
46	KIRKLIN S, SAAL J E, MEREDIG B, et al. The open quantum materials database (OQMD): Assessing the accuracy of DFT formation energies[J]. npj Computational Materials, 2015, 1: doi: 10.1038/npjcompumats.2015.10.
47	GHIRINGHELLI L M, VYBIRAL J, LEVCHENKO S V, et al. Big data of materials science: Critical role of the descriptor[J]. Physical Review Letters, 2015, 114(10): doi: 10.1103/PhysRevLett.114.105503.
48	SENDEK A D, YANG Q, CUBUK E D, et al. Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials[J]. Energy & Environmental Science, 2017, 10(1): 306-320.
49	ZHAO Q, AVDEEV M, CHEN L Q, et al. Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors[J]. Science Bulletin, 2021, 66(14): 1401-1408.
50	WANG A P, ZOU Z Y, WANG D, et al. Identifying chemical factors affecting reaction kinetics in Li-air battery via ab initio calculations and machine learning[J]. Energy Storage Materials, 2021, 35: 595-601.
51	WARD L, AGRAWAL A, CHOUDHARY A, et al. A general-purpose machine learning framework for predicting properties of inorganic materials[J]. npj Computational Materials, 2016, 2: doi: 10.1038/npjcompumats.2016.28.
52	JOSHI R P, EICKHOLT J, LI L L, et al. Machine learning the voltage of electrode materials in metal-ion batteries[J]. ACS Applied Materials & Interfaces, 2019, 11(20): 18494-18503.
53	JO J, CHOI E, KIM M, et al. Machine learning-aided materials design platform for predicting the mechanical properties of Na-ion solid-state electrolytes[J]. ACS Applied Energy Materials, 2021, 4(8): 7862-7869.
54	CHOI E, JO J, KIM W, et al. Searching for mechanically superior solid-state electrolytes in Li-ion batteries via data-driven approaches[J]. ACS Applied Materials & Interfaces, 2021, 13(36): 42590-42597.
55	VERDUZCO J C, MARINERO E E, STRACHAN A. An active learning approach for the design of doped LLZO ceramic garnets for battery applications[J]. Integrating Materials and Manufacturing Innovation, 2021, 10(2): 299-310.
56	WARD L, LIU R Q, KRISHNA A, et al. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations[J]. Physical Review B, 2017, 96(2): doi: 10.1103/PhysRevB.96.024104.
57	WARD L, DUNN A, FAGHANINIA A, et al. Matminer: An open source toolkit for materials data mining[J]. Computational Materials Science, 2018, 152: 60-69.
58	HIMANEN L, JÄGER M O J, MOROOKA E V, et al. DScribe: Library of descriptors for machine learning in materials science[J]. Computer Physics Communications, 2020, 247: doi: 10.1016/j.cpc.2019.106949.
59	RUPP M, TKATCHENKO A, MÜLLER K R, et al. Fast and accurate modeling of molecular atomization energies with machine learning[J]. Physical Review Letters, 2012, 108(5): doi: 10.1103/PhysRevLett.108.058301.
60	FABER F, LINDMAA A, VON LILIENFELD O A, et al. Crystal structure representations for machine learning models of formation energies[J]. International Journal of Quantum Chemistry, 2015, 115(16): 1094-1101.
61	RUPP M. Many-body tensor representation for machine learning of materials[C]//APS March Meeting Abstracts, 2017.
62	BEHLER J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials[J]. The Journal of Chemical Physics, 2011, 134(7): doi: 10.1063/1.3553717.
63	BARTÓK A P, KONDOR R, CSÁNYI G. On representing chemical environments[J]. Physical Review B, 2013, 87(8): doi: 10.1103/physRevB.87.184115.
64	JOLLIFFE I T, CADIMA J. Principal component analysis: A review and recent developments[J]. Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 2016, 374(2065): doi: 10.1098/rsta.2015.0202.
65	THARWAT A, GABER T, IBRAHIM A, et al. Linear discriminant analysis: A detailed tutorial[J]. AI Communications, 2017, 30(2): 169-190.
66	BANGUERO E, CORRECHER A, PÉREZ-NAVARRO Á, et al. Diagnosis of a battery energy storage system based on principal component analysis[J]. Renewable Energy, 2020, 146: 2438-2449.
67	WANG L Y, WANG L F, LIAO C L, et al. Research on multi-parameter evaluation of electric vehicle power battery consistency based on principal component analysis[J]. Journal of Shanghai Jiaotong University (Science), 2018, 23(5): 711-720.
68	CHEN K L, ZHENG F D, JIANG J C, et al. Practical failure recognition model of lithium-ion batteries based on partial charging process[J]. Energy, 2017, 138: 1199-1208.
69	CHANDRASHEKAR G, SAHIN F. A survey on feature selection methods[J]. Computers & Electrical Engineering, 2014, 40(1): 16-28.
70	PENG H C, LONG F H, DING C. Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and Min-redundancy[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238.
71	LI W, JACOBS R, MORGAN D. Predicting the thermodynamic stability of perovskite oxides using machine learning models[J]. Computational Materials Science, 2018, 150: 454-463.
72	LIN F Y, LIANG D, YEH C C, et al. Novel feature selection methods to financial distress prediction[J]. Expert Systems With Applications, 2014, 41(5): 2472-2483.
73	TEKIN ERGUZEL T, TAS C, CEBI M. A wrapper-based approach for feature selection and classification of major depressive disorder-bipolar disorders[J]. Computers in Biology and Medicine, 2015, 64: 127-137.
74	GENUER R, POGGI J M, TULEAU-MALOT C. Variable selection using random forests[J]. Pattern Recognition Letters, 2010, 31(14): 2225-2236.
75	LIU Y, WU J M, AVDEEV M, et al. Multi-layer feature selection incorporating weighted score-based expert knowledge toward modeling materials with targeted properties[J]. Advanced Theory and Simulations, 2020, 3(2): doi: 10.1002/adts.201900215.
76	GHARAGHEIZI F, SATTARI M, ILANI-KASHKOULI P, et al. A "non-linear" quantitative structure-property relationship for the prediction of electrical conductivity of ionic liquids[J]. Chemical Engineering Science, 2013, 101: 478-485.
77	WU H, LORENSON A, ANDERSON B, et al. Robust FCC solute diffusion predictions from ab-initio machine learning methods[J]. Computational Materials Science, 2017, 134: 160-165.
78	SHANDIZ M A, GAUVIN R. Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries[J]. Computational Materials Science, 2016, 117: 270-278.
79	ZHAO Q, ZHANG L W, HE B, et al. Identifying descriptors for Li⁺ conduction in cubic Li-argyrodites via hierarchically encoding crystal structure and inferring causality[J]. Energy Storage Materials, 2021, 40: 386-393.
80	UNCU Ö, TÜRKŞEN I B. A novel feature selection approach: Combining feature wrappers and filters[J]. Information Sciences, 2007, 177(2): 449-466.
81	HSU H H, HSIEH C W, LU M D. Hybrid feature selection by combining filters and wrappers[J]. Expert Systems With Applications, 2011, 38(7): 8144-8150.
82	NASRABADI N M. Book review: Pattern recognition and machine learning[J]. Journal of Electronic Imaging, 2007, 16: doi: 10.1117/1.2819119.
83	XU Y J, ZONG Y, HIPPALGAONKAR K. Machine learning-assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors using facile descriptors[J]. Journal of Physics Communications, 2020, 4(5): doi: 10.1088/2399-6528/ab92d8.
84	LIU B, YANG J, YANG H L, et al. Rationalizing the interphase stability of Li\|doped-Li₇La₃Zr₂O₁₂via automated reaction screening and machine learning[J]. Journal of Materials Chemistry A, 2019, 7(34): 19961-19969.
85	KIREEVA N, PERVOV V S. Materials informatics screening of Li-rich layered oxide cathode materials with enhanced characteristics using synthesis data[J]. Batteries & Supercaps, 2020, 3(5): 427-438.
86	NAGULAPATI V M, LEE H, JUNG D, et al. A novel combined multi-battery dataset based approach for enhanced prediction accuracy of data driven prognostic models in capacity estimation of lithium ion batteries[J]. Energy and AI, 2021, 5: doi: 10.1088/2399-6528/ab92018.
87	FUJIMURA K, SEKO A, KOYAMA Y, et al. Accelerated materials design of lithium superionic conductors based on first-principles calculations and machine learning algorithms[J]. Advanced Energy Materials, 2013, 3(8): 980-985.
88	DUQUESNOY M, BOYANO I, GANBORENA L, et al. Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity[J]. Energy and AI, 2021, 5: doi: 10.1016/j.egyai.2021.100090.
89	ISHIKAWA A, SODEYAMA K, IGARASHI Y, et al. Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents[J]. Physical Chemistry Chemical Physics: PCCP, 2019, 21(48): 26399-26405.
90	HOMMA K, LIU Y, SUMITA M, et al. Optimization of a heterogeneous ternary Li₃PO₄-Li₃BO₃-Li₂SO₄ mixture for Li-ion conductivity by machine learning[J]. The Journal of Physical Chemistry C, 2020, 124(24): 12865-12870.
91	ZHANG Y, TANG Q, ZHANG Y, et al. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning[J]. Nature Communications, 2020, 11: doi: 10.1038/s41467-020-15235-7.
92	WANG Z L, ZHANG H K, LI J J. Accelerated discovery of stable spinels in energy systems via machine learning[J]. Nano Energy, 2021, 81: doi: 10.1016/j.nanoen.2020.105665.
93	LEE B, YOO J, KANG K. Predicting the chemical reactivity of organic materials using a machine-learning approach[J]. Chemical Science, 2020, 11(30): 7813-7822.
94	DUONG V M, TRAN T N, GARG A, et al. Machine learning technique-based data-driven model of exploring effects of electrolyte additives on LiNi_0.6Mn_0.2Co_0.2O₂/graphite cell[J]. Journal of Energy Storage, 2021, 42: doi: 10.1016/j.est.2021.103012.
95	MOSES I A, JOSHI R P, OZDEMIR B, et al. Machine learning screening of metal-ion battery electrode materials[J]. ACS Applied Materials & Interfaces, 2021, 13(45): 53355-53362.
96	刘亚利, 吴娇杨, 李泓. 锂离子电池基础科学问题(Ⅸ)——非水液体电解质材料[J]. 储能科学与技术, 2014, 3(3): 262-282.
	LIU Y L, WU J Y, LI H. Fundamental scientific aspects of lithium ion batteries (Ⅸ)—Nonaqueous electrolyte materials[J]. Energy Storage Science and Technology, 2014, 3(3): 262-282.
97	ZHANG J G, XU W, XIAO J, et al. Lithium metal anodes with nonaqueous electrolytes[J]. Chemical Reviews, 2020, 120(24): 13312-13348.
98	LI Y Y, STROE D I, CHENG Y H, et al. On the feature selection for battery state of health estimation based on charging-discharging profiles[J]. Journal of Energy Storage, 2021, 33: doi: 10.1016/j.est.2020.102122.
99	ATTIA P M, GROVER A, JIN N, et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning[J]. Nature, 2020, 578(7795): 397-402.
100	GONG W Y, YAN Q M. Graph-based deep learning frameworks for molecules and solid-state materials[J]. Computational Materials Science, 2021, 195: doi: 10.1016/j.commatsci.2021.110332.
101	TRÉMEAU A, XU S X, MUSELET D. Deep Learning for Material recognition: Most recent advances and open challenges[C]//International Conference on "Big Data, Machine Learning and Applications" (BIGDML), Silchar, India, 2019.
102	DUVENAUD D, MACLAURIN D, AGUILERA-IPARRAGUIRRE J, et al. Convolutional networks on graphs for learning molecular fingerprints[C]// Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 2224-2232.
103	WU Z Q, RAMSUNDAR B, FEINBERG E N, et al. MoleculeNet: a benchmark for molecular machine learning[J]. Chemical Science, 2017, 9(2): 513-530.
104	XIE T, GROSSMAN J C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties[J]. Physical Review Letters, 2018, 120(14): doi: 10.1103/PhysRevLett.120.145301.
105	KARAMAD M, MAGAR R, SHI Y T, et al. Orbital graph convolutional neural network for material property prediction[J]. Physical Review Materials, 2020, 4(9): doi: 10.1103/PhysRevMaterials. 4.093801.
106	BANJADE H R, HAURI S, ZHANG S S, et al. Structure motif-centric learning framework for inorganic crystalline systems[J]. Science Advances, 2021, 7(17): doi: 10.1126/sciadv.abf1754.
107	CHEN C, YE W K, ZUO Y X, et al. Graph networks as a universal machine learning framework for molecules and crystals[J]. Chemistry of Materials, 2019, 31(9): 3564-3572.
108	PARK C W, WOLVERTON C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery[J]. Physical Review Materials, 2020, 4(6): doi: 10.1103/PhysRevMaterials.4.063801.
109	AHMAD Z, XIE T, MAHESHWARI C, et al. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes[J]. ACS Central Science, 2018, 4(8): 996-1006.
110	ZHOU L M, YAO A M, WU Y J, et al. Machine learning assisted prediction of cathode materials for Zn-ion batteries[J]. Advanced Theory and Simulations, 2021, 4(9): doi: 10.1002/adts.202100196.
111	CHEN C, ZUO Y, YE W, et al. Learning properties of ordered and disordered materials from multi-fidelity data[J]. Nature Computational Science, 2021, 1(1): 46-53.
112	LI S N, LIU Y J, CHEN D, et al. Encoding the atomic structure for machine learning in materials science[J]. WIREs Computational Molecular Science, 2022, 12(1): doi: 10.1002/wcms.1558.
113	PIETSCH P, WOOD V. X-ray tomography for lithium ion battery research: A practical guide[J]. Annual Review of Materials Research, 2017, 47: 451-479.
114	DIXIT M B, VERMA A, ZAMAN W, et al. Synchrotron imaging of pore formation in Li metal solid-state batteries aided by machine learning[J]. ACS Applied Energy Materials, 2020, 3(10): 9534-9542.
115	JIANG Z, LI J, YANG Y, et al. Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes[J]. Nature Communications, 2020, 11: doi: 10.1038/s41467-020-16233-5.
116	BALIYAN A, IMAI H. Machine learning based analytical framework for automatic hyperspectral Raman analysis of lithium-ion battery electrodes[J]. Scientific Reports, 2019, 9: doi: 10.1038/s41598-019-54770-2.
117	FURAT O, FINEGAN D P, DIERCKS D, et al. Mapping the architecture of single lithium ion electrode particles in 3D, using electron backscatter diffraction and machine learning segmentation[J]. Journal of Power Sources, 2021, 483: doi: 10.1016/j.jpowsour. 2020.229148.
118	KHATAVKAR N, SWETLANA S, SINGH A K. Accelerated prediction of Vickers hardness of Co- and Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning[J]. Acta Materialia, 2020, 196: 295-303.
119	KONONOVA O, HE T J, HUO H Y, et al. Opportunities and challenges of text mining in materials research[J]. iScience, 2021, 24(3): doi: 10.1016/j.isci.2021.102155.
120	TORAYEV A, MAGUSIN P C M M, GREY C P, et al. Text mining assisted review of the literature on Li-O₂ batteries[J]. Journal of Physics: Materials, 2019, 2(4): doi: 10.1088/2515-7639/ab3611.
121	EL-BOUSIYDY H, LOMBARDO T, PRIMO E N, et al. What can text mining tell us about lithium-ion battery researchers' habits? [J]. Batteries & Supercaps, 2021, 4(5): doi: /10.1002/batt.202000288.
122	NIE Z W, LIU Y J, YANG L Y, et al. Construction and application of materials knowledge graph based on author disambiguation: Revisiting the evolution of LiFePO 4[J]. Advanced Energy Materials, 2021, 11(16): doi: 10.1002/aenm.202003580.
123	ZHU Y Z, HE X F, MO Y F. Origin of outstanding stability in the lithium solid electrolyte materials: Insights from thermodynamic analyses based on first-principles calculations[J]. ACS Applied Materials & Interfaces, 2015, 7(42): 23685-23693.
124	MAHBUB R, HUANG K, JENSEN Z, et al. Text mining for processing conditions of solid-state battery electrolytes[J]. Electrochemistry Communications, 2020, 121: doi: 10.1016/j.elecom.2020.106860.
125	SWAIN M C, COLE J M. ChemDataExtractor: A toolkit for automated extraction of chemical information from the scientific literature[J]. Journal of Chemical Information and Modeling, 2016, 56(10): 1894-1904.
126	HUANG S, COLE J M. A database of battery materials auto-generated using ChemDataExtractor[J]. Scientific Data, 2020, 7: doi: 10.1038/s41597-020-00602-2.
127	周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.
	ZHOU Z H. Machine learning[M]. Beijing: Tsinghua University Press, 2016.
128	VAN DYK D A, MENG X L. The art of data augmentation[J]. Journal of Computational and Graphical Statistics, 2001, 10(1): 1-50.
129	NAAZ F, HERLE A, CHANNEGOWDA J, et al. A generative adversarial network-based synthetic data augmentation technique for battery condition evaluation[J]. International Journal of Energy Research, 2021, 45(13): 19120-19135.
130	HSU T, EPTING W K, KIM H, et al. Microstructure generation via generative adversarial network for heterogeneous, topologically complex 3D materials[J]. JOM, 2021, 73(1): 90-102.
131	LOOKMAN T, BALACHANDRAN P V, XUE D, et al. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design[J]. Npj Computational Materials, 2019, 5: doi: 10.1038/s41524-019-0153-8.
132	ZHUANG F Z, QI Z Y, DUAN K Y, et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE, 2021, 109(1): 43-76.
133	WANG Z L, WANG Q X, HAN Y Q, et al. Deep learning for ultra-fast and high precision screening of energy materials[J]. Energy Storage Materials, 2021, 39: 45-53.
134	THORNTON C, HUTTER F, HOOS H H, et al. Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms[C]//KDD '13: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013: 847-855.
135	FEURER M, KLEIN A, EGGENSPERGER K, et al. Auto-sklearn: Efficient and Robust Automated Machine LearningAutomated Machine Learning[M]. USA: Springer, 2019: 113-134.
136	DUNN A, WANG Q, GANOSE A, et al. Benchmarking materials property prediction methods: The Matbench test set and Automatminer reference algorithm[J]. Npj Computational Materials, 2020, 6: doi: 10.1038/s41524-020-00406-3.
137	DENKER J, SCHWARTZ D, WITTNER B, et al. Large automatic learning, rule extraction, and generalization[J]. Complex Systems, 1987, 1: 877-922.
138	KOH P W, LIANG P. Understanding black-box predictions via influence functions[C]//34th International Conference on Machine Learning, 2017: 1885-1894.
139	FRYE C, FEIGE I, ROWAT C. Asymmetric shapley values: Incorporating causal knowledge into model-agnostic explainability[J]. Advances in Neural Information Processing Systems, 2020, 33: 1229-1239.
140	LI Y H, XIAO B, TANG Y C, et al. Center-environment feature model for machine learning study of spinel oxides based on first-principles computations[J]. The Journal of Physical Chemistry C, 2020, 124(52): 28458-28468.
141	WENG B, SONG Z, ZHU R, et al. Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts[J]. Nature Communications, 2020, 11: doi: 10.1038/s41467-020-17263-9.
142	GONG S, WANG S, ZHU T S, et al. Screening and understanding Li adsorption on two-dimensional metallic materials by learning physics and physics-simplified learning[J]. JACS, 2021, 1(11): 1904-1914.
143	FLORES M J, NICHOLSON A E, BRUNSKILL A, et al. Incorporating expert knowledge when learning Bayesian network structure: A medical case study[J]. Artificial Intelligence in Medicine, 2011, 53(3): 181-204.
144	TANG W Y, MAO K Z, MAK L O, et al. Adaptive fuzzy rule-based classification system integrating both expert knowledge and data[C]//2012 IEEE 24th International Conference on Tools with Artificial Intelligence, Athens, Greece. IEEE, 2012: 814-821.
145	REN Z, OVIEDO F, THWAY M, et al. Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics[J]. npj Computational Materials, 2020, 6: doi: 10.1038/s41524-020-0277-x
146	GHALLAB M, SPYROPOULOS C D, FAKOTAKIS N, et al. Fighting knowledge acquisition bottleneck with argument based machine learning[J]. Frontiers in Artificial Intelligence and Applications, 2008, 178: 234-238.
147	LIU Y, WU J M, WANG Z C, et al. Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning[J]. Acta Materialia, 2020, 195: 454-467.

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数据驱动的机器学习在电化学储能材料研究中的应用

Applying data-driven machine learning to studying electrochemical energy storage materials

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