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J. Phys. Energy 2 (2020) 032008 M Pasta et al [100] [101] [102] [103] [104] [105] [106] [107] [108] [109] [110] [111] [112] [113] [114] [115] [116] [117] [118] [119] [120] [121] [122] [123] [124] [125] [126] [127] [128] [129] [130] [131] [132] [133] [134] [135] [136] Ohno S, Koerver R, Dewald G, Rosenbach C, Titscher P, Steckermeier D, Kwade A, Janek J and Zeier W G 2019 Observation of chemo-mechanical failure and influence of cut-off potentials in all-solid-state Li-S batteries Chem. Mater. 31 2930–40 McClelland I, Cussen E J and Corr S A 2020 Muon spectroscopy for energy storage materials Annual Review of Materials Science 50 371–93 Pfenninger R, Struzik M, Garbayo I, Stilp E and Rupp J L 2019 A low ride on processing temperature for fast lithium conduction in garnet solid-state battery films Nature Energy 4 475–83 Dietrich C, Weber D A, Sedlmaier S J, Indris S, Culver S P, Walter D, Janek J and Zeier W G 2017 Lithium ion conductivity in Li2S-P2S5 glasses-building units and local structure evolution during the crystallization of superionic conductors Li3PS4, Li7P3S11 and Li4P2S7 J. Mater. Chem. A 5 18111–19 Kato Y, Hori S, Saito T, Suzuki K, Hirayama M, Mitsui A, Yonemura M, Iba H and Kanno R 2016 High-power all-solid-state batteries using sulfide superionic conductors Nature Energy 1 16030 Deiseroth H J, Kong S T, Eckert H, Vannahme J, Reiner C, Zaiß T and Schlosser M 2008 Li6PS5X: A class of crystalline Li-rich solids with an unusually high Li+ mobility Angewandte Chemie - Int. edn 47 755–8 Kraft M A et al 2017 Influence of lattice polarizability on the ionic conductivity in the lithium superionic argyrodites Li6PS5X (X = Cl, Br, I) J. Am. Chem. Soc. 139 10909–18 Zhou L, Assoud A, Zhang Q, Wu X and Nazar L F 2019 New Family of Argyrodite Thioantimonate Lithium Superionic Conductors J. Am. Chem. Soc. 141 19002–13 Yu C et al 2018 Facile synthesis toward the optimal structure-conductivity characteristics of the argyrodite Li6PS5Cl solid-state electrolyte ACS Appl. Mater. Interfaces 10 33296–306 Yu C, Ganapathy S, De Klerk Roslon I, Van Eck E R, Kentgens A P and Wagemaker M 2016 Unravelling Li-Ion transport from picoseconds to seconds: bulk versus interfaces in an argyrodite Li6PS5Cl-Li2S all-solid-state Li-Ion battery J. Am. Chem. Soc. 138 11192–201 Yang X G, Liu T, Gao Y, Ge S, Leng Y, Wang D and Wang C Y 2019 Asymmetric temperature modulation for extreme fast charging of lithium-ion batteries Joule 3 3002–19 Wang P, Qu W, Song W L, Chen H, Chen R and Fang D 2019 Electro–chemo–mechanical issues at the interfaces in solid-state lithium metal batteries Adv. Funct. Mater. 29 1900950 Nyman J and Reutzel-Edens S M 2018 Crystal structure prediction is changing from basic science to applied technology Faraday Discuss. 211 459–76 Lonie D C and Zurek E 2011 XtalOpt: An open-source evolutionary algorithm for crystal structure prediction Comput. Phys. Commun. 182 372–87 Pickard C J and Needs R J 2011 Ab initio random structure searching J. Phys. Condensed Matter 23 053201 Gamon J et al 2019 Computationally guided discovery of the sulfide Li3AlS3 in the Li-Al-S phase field: structure and lithium conductivity Chem. Mater. 31 9699–9714 Collins C, Dyer M S, Pitcher M J, Whitehead G F S, Zanella M, Mandal P, Claridge J B, Darling G R and Rosseinsky M J 2017 Accelerated discovery of two crystal structure types in a complex inorganic phase field Nature 546 280–4 Jia X et al 2019 Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis Nature 573 251–5 Schmidt J, Marques M R, Botti S and Marques M A 2019 Recent advances and applications of machine learning in solid-state materials science npj Computational Materials 5 83 Schleder G R, Padilha A C M, Reily Rocha A, Dalpian G M and Fazzio A 2020 Ab Initio simulations and materials chemistry in the age of big data J. Chem. Inform. Modeling 60 452–9 Haghighatlari M, Li J, Heidar-Zadeh F, Liu Y, Guan X and Head-Gordon T 2020 Learning to make chemical predictions: the interplay of feature representation, data and machine learning algorithms arXiv:2003.00157 Himanen L, J ̈ager M O, Morooka E V, Federici Canova F, Ranawat Y S, Gao D Z, Rinke P and Foster A S 2020 DScribe: Library of descriptors for machine learning in materials science Comput. Phys. Commun. 247 106949 Sendek A D, Yang Q, Cubuk E D, Duerloo K A N, Cui Y and Reed E J 2017 Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials Energy Environ. Sci. 10 306–20 Dave A, Mitchell J, Kandasamy K, Burke S, Paria B, Poczos B, Whitacre J and Viswanathan V 2019 Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning arXiv:2001.09938 Cubuk E D, Sendek A D and Reed E J 2019 Screening billions of candidates for solid lithium-ion conductors A transfer learning approach for small data J. Chem. Phys. 150 214701 Kerman K, Luntz A, Viswanathan V, Chiang Y M and Chen Z 2017 Review—practical challenges hindering the development of solid state Li Ion batteries J. Electrochem. Soc. 164 A1731–44 Thangadurai V, Narayanan S and Pinzaru D 2014 Garnet-type solid-state fast Li ion conductors for Li batteries: critical review Chem. Soc. Rev. 43 4714 Fu K et al 2017 Three-dimensional bilayer garnet solid electrolyte based high energy density lithium metal-sulfur batteries Energy Environ. Sci. 10 1568–75 Han X et al 2017 Negating interfacial impedance in garnet-based solid-state Li metal batteries Nat. Mater. 16 572–9 Qian J, Adams B D, Zheng J, Xu W, Henderson W A, Wang J, Bowden M E, Xu S, Hu J and Zhang J-G 2016 Anode-free rechargeable lithium metal batteries Adv. Funct. Mater. 26 7094–102 Bu J, Leung P, Huang C, Lee S H and Grant P S 2019 Co-spray printing of LiFePO4 and PEO-Li1.5Al0.5Ge1.5(PO4)3 hybrid electrodes for all-solid-state Li-ion battery applications J. Mater. Chem. 7 19094–103 Leung P, Bu J, Quijano Velasco P, Roberts M R, Grobert N and Grant P S 2019 Single-step spray printing of symmetric all-organic solid-state batteries based on Porous textile dye electrodes Adv. Energy Mater. 9 1901418 Kim D H, Oh D Y, Park K H, Choi Y E, Nam Y J, Lee H A, Lee S M and Jung Y S 2017 Infiltration of solution-processable solid electrolytes into conventional Li-Ion-battery electrodes for all-solid-state Li-Ion batteries Nano Lett. 17 3013–20 Advanced Research Projects Agency-Energy (ARPA-E) U D o E 2016 Integration and optimization of novel ion conducting solids (IONICS) (Washington, DC: Advanced Research Projects Agency Energy) Liang X, Tan F, Wei F and Du J 2019 Research progress of all solid-state thin film lithium battery IOP Conf. Ser.: Earth Environ. Sci. 218 012138 Hao F, Han F, Liang Y, Wang C and Yao Y 2018 Architectural design and fabrication approaches for solid-state batteries MRS Bull. 43 746–51 Ren Y, Shen Y, Lin Y and Nan C W 2019 Microstructure manipulation for enhancing the resistance of garnet-type solid electrolytes to “short circuit” by Li metal anodes ACS Appl. Mater. Interfaces 11 5928–37 51PDF Image | 2020 roadmap on solid-state batteries
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