基于大数据的电池新材料设计

doi:10.19799/j.cnki.2095-4239.2024.0565

摘要/Abstract

摘要：

固态电池是极具潜力的下一代储能器件之一，寻找综合性能优异的电池材料有望从根本上提升电池的性能。本文围绕固态电池中离子传输、表面/界面现象以及微观结构动态演化等关键科学问题，介绍了基于多精度传递思想的高通量材料筛选策略，以及机器学习技术在加速模拟复杂物理化学过程、解析电池内部复杂构效关系的突出作用。受益于多精度传递思想和机器学习技术的应用，可以从直接筛选、元素替换、结构单元搭建、非晶结构构建等多个角度高效获得快离子导体材料，多维度解析离子传输性能与微观机制，极大丰富了电极材料、固态电解质材料等候选范围。此外，针对电池材料研发流程的云工具箱提供了数据归档、分析及复用等多项功能，旨在使得借助大数据和人工智能的材料研发流程更为自动化。本文介绍的基于大数据的研究思路和研究模式，结合新兴的机器学习技术，能够有效加速新型电池材料的设计和开发进程，深化对电池内部复杂物理化学现象的理解，为设计实用的新型固态电池材料赋能。

关键词: 储能材料, 材料设计, 高通量计算, 大数据, 人工智能

Abstract:

Solid-state batteries are one of the most promising next-generation energy storage technologies. Designing new battery materials with excellent comprehensive performance is expected to improve the performance of batteries. This article focuses on key scientific issues in solid-state batteries, such as migrated ion transport behavior, complex surface/interface phenomena, and microstructural dynamic evolution. Furthermore, it proposes a high-throughput material screening strategy based on multiprecision transmission. It also discusses the outstanding role of machine learning techniques in accelerating the simulation of complex physical and chemical processes and analyzing the structure-properties relationships of batteries. Benefiting from the application of multiprecision transmission concepts and machine learning techniques, the efficient acquisition of fast ion conductor materials can be achieved from various perspectives, such as direct screening, element substitution, crystal structure construction, and the design of amorphous structures. This allows for a multidimensional analysis of ionic transport performance and microscopic mechanisms, greatly enriching the range of candidate materials for electrode and solid-state electrolyte materials. In addition, the cloud toolkit designed for the development process of battery materials provides multiple features, such as data archiving, analysis, and reutilization, aimed at automating the material development process. The research paradigm and models based on big data introduced in this article, together with emerging machine learning technologies, can effectively accelerate the design and development process of new battery materials, deepen the understanding of complex physical and chemical phenomena inside batteries, and promote the design of practical new solid-state battery materials.

Key words: energy storage materials, material design, high-throughput calculations, big data, artificial intelligence

中图分类号:

许晶, 王宇琦, 符晓, 杨其凡, 连景臣, 王力奇, 肖睿娟. 基于大数据的电池新材料设计[J]. 储能科学与技术, 2024, 13(9): 2920-2932.

Jing XU, Yuqi WANG, Xiao FU, Qifan YANG, Jingchen LIAN, Liqi WANG, Ruijuan XIAO. Discovery of new battery materials based on a big data approach[J]. Energy Storage Science and Technology, 2024, 13(9): 2920-2932.

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参考文献 52

1	WU D X, CHEN L Q, LI H, et al. Recent progress of solid-state lithium batteries in China[J]. Applied Physics Letters, 2022, 121(12): 120502. DOI: 10.1063/5.0117248.
2	HUANG Y L, CAO B W, GENG Z, et al. Advanced electrolytes for rechargeable lithium metal batteries with high safety and cycling stability[J]. Accounts of Materials Research, 2024, 5(2): 184-193. DOI: 10.1021/accountsmr.3c00232.
3	WU D X, CHEN L Q, LI H, et al. Solid-state lithium batteries-from fundamental research to industrial progress[J]. Progress in Materials Science, 2023, 139: 101182. DOI: 10.1016/j.pmatsci. 2023.101182.
4	WU E A, BANERJEE S, TANG H M, et al. A stable cathode-solid electrolyte composite for high-voltage, long-cycle-life solid-state sodium-ion batteries[J]. Nature Communications, 2021, 12(1): 1256. DOI: 10.1038/s41467-021-21488-7.
5	MIAO X, GUAN S D, MA C, et al. Role of interfaces in solid-state batteries[J]. Advanced Materials, 2023, 35(50): 2206402. DOI: 10.1002/adma.202206402.
6	LUO Z, QIU X J, LIU C, et al. Interfacial challenges towards stable Li metal anode[J]. Nano Energy, 2021, 79: 105507. DOI: 10.1016/j.nanoen.2020.105507.
7	SHEN X H, TIAN Z Y, FAN R J, et al. Research progress on silicon/carbon composite anode materials for lithium-ion battery[J]. Journal of Energy Chemistry, 2018, 27(4): 1067-1090. DOI: 10.1016/j.jechem.2017.12.012.
8	NAM H G, PARK J Y, YUK J M, et al. Phase transformation mechanism and stress evolution in Sn anode[J]. Energy Storage Materials, 2022, 45: 101-109. DOI: 10.1016/j.ensm.2021.11.034.
9	GOODENOUGH J B, KIM Y. Challenges for rechargeable Li batteries[J]. Chemistry of Materials, 2010, 22(3): 587-603. DOI: 10.1021/cm901452z.
10	CHEN L H, VENKATRAM S, KIM C, et al. Electrochemical stability window of polymeric electrolytes[J]. Chemistry of Materials, 2019, 31(12): 4598-4604. DOI: 10.1021/acs.chemmater.9b01553.
11	ZHU Y Z, HE X F, MO Y F. First principles study on electrochemical and chemical stability of solid electrolyte-electrode interfaces in all-solid-state Li-ion batteries[J]. Journal of Materials Chemistry A, 2016, 4(9): 3253-3266. DOI: 10.1039/C5TA08574H.
12	GU L, ZHU C B, LI H, et al. Direct observation of lithium staging in partially delithiated LiFePO₄ at atomic resolution[J]. Journal of the American Chemical Society, 2011, 133(13): 4661-4663. DOI: 10.1021/ja109412x.
13	ONUMA H, KUBOTA K, MURATSUBAKI S, et al. Phase evolution of electrochemically potassium intercalated graphite[J]. Journal of Materials Chemistry A, 2021, 9(18): 11187-11200. DOI: 10.1039/D0TA12607A.
14	HE Z Y, ZHANG C X, ZHU Y K, et al. The acupuncture effect of carbon nanotubes induced by the volume expansion of silicon-based anodes[J]. Energy & Environmental Science, 2024, 17(10): 3358-3364. DOI: 10.1039/D4EE00710G.
15	NOMURA Y, YAMAMOTO K, YAMAGISHI Y, et al. Lithium transport pathways guided by grain architectures in Ni-rich layered cathodes[J]. ACS Nano, 2021, 15(12): 19806-19814. DOI: 10.1021/acsnano.1c07252.
16	WANG Y, RICHARDS W D, ONG S P, et al. Design principles for solid-state lithium superionic conductors[J]. Nature Materials, 2015, 14(10): 1026-1031. DOI: 10.1038/nmat4369.
17	刘振东, 潘嘉杰, 刘全兵. 机器学习在设计高性能锂电池正极材料与电解质中的应用[J]. 化学进展, 2023, 35(4): 577-592.
	LIU Z D, PAN J J, LIU Q B. Application of machine learning in the design of cathode materials and electrolytes for high-performance lithium batteries[J]. Progress in Chemistry, 2023, 35(4): 577-592.
18	XIAO R J, LI H, CHEN L Q. High-throughput design and optimization of fast lithium ion conductors by the combination of bond-valence method and density functional theory[J]. Scientific Reports, 2015, 5: 14227. DOI: 10.1038/srep14227.
19	ADAMS S, RAO R P. High power lithium ion battery materials by computational design[J]. Physica Status Solidi (a), 2011, 208(8): 1746-1753. DOI: 10.1002/pssa.201001116.
20	CHEN H M, ADAMS S. Bond softness sensitive bond-valence parameters for crystal structure plausibility tests[J]. IUCrJ, 2017, 4(5): 614-625. DOI: 10.1107/s2052252517010211.
21	WANG Y Q, SUN X R, XIAO R J, et al. Computational screening of doping schemes for LiTi₂(PO₄)₃ as cathode coating materials[J]. Chinese Physics B, 2020, 29(3): 038202. DOI: 10.1088/1674-1056/ab7186.
22	HENKELMAN G, UBERUAGA B P, JÓNSSON H. A climbing image nudged elastic band method for finding saddle points and minimum energy paths[J]. 2000, 113(22): 9901-9904. DOI: 10. 1063/1.1329672.
23	RONG Z Q, KITCHAEV D, CANEPA P, et al. An efficient algorithm for finding the minimum energy path for cation migration in ionic materials[J]. 2016, 145(7): 074112. DOI: 10. 1063/1.4960790.
24	HE X F, ZHU Y Z, EPSTEIN A, et al. Statistical variances of diffusional properties from ab initio molecular dynamics simulations[J]. NPJ Computational Materials, 2018, 4: 18. DOI: 10.1038/s41524-018-0074-y.
25	WANG S, BAI Q, NOLAN A M, et al. Lithium chlorides and bromides as promising solid-state chemistries for fast ion conductors with good electrochemical stability[J]. Angewandte Chemie International Edition, 2019, 58(24): 8039-8043. DOI: 10.1002/anie.201901938.
26	WANG Y Q, WU S Y, SHAO W, et al. Accelerated strategy for fast ion conductor materials screening and optimal doping scheme exploration[J]. Journal of Materiomics, 2022, 8(5): 1038-1047. DOI: 10.1016/j.jmat.2022.02.010.
27	BARTÓK A P, KONDOR R, CSÁNYI G. On representing chemical environments[J]. Physical Review B, 2013, 87(18): 184115. DOI: 10.1103/physrevb.87.184115.
28	BOTU V, RAMPRASAD R. Learning scheme to predict atomic forces and accelerate materials simulations[J]. Physical Review B, 2015, 92(9): 094306. DOI: 10.1103/physrevb.92.094306.
29	WANG H, ZHANG L F, HAN J Q, et al. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics[J]. Computer Physics Communications, 2018, 228: 178-184. DOI: 10.1016/j.cpc.2018.03.016.
30	DENG B W, ZHONG P C, JUN K, et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling[J]. Nature Machine Intelligence, 2023, 5: 1031-1041. DOI: 10.1038/s42256-023-00716-3.
31	BATZNER S, MUSAELIAN A, SUN L X, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials[J]. Nature Communications, 2022, 13: 2453. DOI: 10.1038/s41467-022-29939-5.
32	BEHLER J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials[J]. Journal of Chemical Physics, 2011, 134(7): 074106. DOI: 10.1063/1.3553717.
33	BEHLER J. Four generations of high-dimensional neural network potentials[J]. Chemical Reviews, 2021, 121(16): 10037-10072. DOI: 10.1021/acs.chemrev.0c00868.
34	GASTEGGER M, SCHWIEDRZIK L, BITTERMANN M, et al. WACSF-Weighted atom-centered symmetry functions as descriptors in machine learning potentials[J]. Journal of Chemical Physics, 2018, 148(24): 241709. DOI: 10.1063/1.5019667.
35	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): 145301. DOI: 10.1103/PhysRevLett.120.145301.
36	LIU Y, YANG Z W, ZOU X X, et al. Data quantity governance for machine learning in materials science[J]. National Science Review, 2023, 10(7): nwad125. DOI: 10.1093/nsr/nwad125.
37	LIU Y S, HE X F, MO Y F. Discrepancies and error evaluation metrics for machine learning interatomic potentials[J]. NPJ Computational Materials, 2023, 9: 174. DOI: 10.1038/s41524-023-01123-3.
38	MUSAELIAN A, BATZNER S, JOHANSSON A, et al. Learning local equivariant representations for large-scale atomistic dynamics[J]. Nature Communications, 2023, 14(1): 579. DOI: 10. 1038/s41467-023-36329-y.
39	XIAO R J, LI H, CHEN L Q. High-throughput computational discovery of K₂CdO₂ as an ion conductor for solid-state potassium-ion batteries[J]. Journal of Materials Chemistry A, 2020, 8(10): 5157-5162. DOI: 10.1039/C9TA13105A.
40	HE J G, YAO Z P, HEGDE V I, et al. Computational discovery of stable heteroanionic oxychalcogenides ABXO (A, B = metals; X = S, Se, and Te) and their potential applications[J]. Chemistry of Materials, 2020, 32(19): 8229-8242. DOI: 10.1021/acs.chemmater.0c01902.
41	FU X, WANG Y Q, XU J, et al. First-principles study on a new chloride solid lithium-ion conductor material with high ionic conductivity[J]. Journal of Materials Chemistry A, 2024, 12(17): 10562-10570. DOI: 10.1039/D3TA07943K.
42	WANG X L, XIAO R J, LI H, et al. Oxysulfide LiAlSO: A lithium superionic conductor from first principles[J]. Physical Review Letters, 2017, 118(19): 195901. DOI: 10.1103/PhysRevLett.118.195901.
43	WANG Y C, LV J, ZHU L, et al. CALYPSO: A method for crystal structure prediction[J]. Computer Physics Communications, 2012, 183(10): 2063-2070. DOI: 10.1016/j.cpc.2012.05.008.
44	XU J, WANG Y Q, WU S Y, et al. New halide-based sodium-ion conductors Na₃Y₂Cl₉ inversely designed by building block construction[J]. ACS Applied Materials & Interfaces, 2023, 15(17): 21086-21096. DOI: 10.1021/acsami.3c01570.
45	WAROQUIERS D, GONZE X, RIGNANESE G M, et al. Statistical analysis of coordination environments in oxides[J]. Chemistry of Materials, 2017, 29(19): 8346-8360. DOI: 10.1021/acs.chemmater.7b02766.
46	WU S Y, XIAO R J, LI H, et al. Ionic conductivity of LiSiON and the effect of amorphization/heterovalent doping on Li⁺ diffusion[J]. Inorganics, 2022, 10(4): 45. DOI: 10.3390/inorganics10040045.
47	LIN L Y, GUO W, LI M J, et al. Progress and perspective of glass-ceramic solid-state electrolytes for lithium batteries[J]. Materials, 2023, 16(7): 2655. DOI: 10.3390/ma16072655.
48	DAI T, WU S Y, LU Y X, et al. Inorganic glass electrolytes with polymer-like viscoelasticity[J]. Nature Energy, 2023, 8: 1221-1228. DOI: 10.1038/s41560-023-01356-y.
49	XU J, XIAO R, LI H. ESM cloud toolkit: A copilot for energy storage material research[J]. Chinese Physics Letters, 2024, 41(5): 054701. DOI: 10.1088/0256-307x/41/5/054701.
50	王丹, 周明波, 黄东宸, 等. 凝聚态物质科学科研数据管理与应用[J]. 科学通报, 2024, 69(9): 1164-1174.
	WANG D, ZHOU M B, HUANG D C, et al. Management and application of research data in condensed matter science[J]. Chinese Science Bulletin, 2024, 69(9): 1164-1174.
51	LIU Y, LIU D H, GE X Y, et al. A high-quality dataset construction method for text mining in materials science[J]. Acta Physica Sinica, 2023, 72(7): 070701. DOI: 10.7498/aps.72.20222316.
52	刘悦, 邹欣欣, 杨正伟, 等. 材料领域知识嵌入的机器学习[J]. 硅酸盐学报, 2022, 50(3): 863-876. DOI: 10.14062/j.issn.0454-5648. 20220093.
	LIU Y, ZOU X X, YANG Z W, et al. Machine learning embedded with materials domain knowledge[J]. Journal of the Chinese Ceramic Society, 2022, 50(3): 863-876. DOI: 10.14062/j.issn. 0454-5648.20220093.

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