journal article·2024-06

Discovering virtual antiperovskites as solid-state electrolytes through active learning

Byung Do Lee, Jiyoon Shin, Seonghwan Kim, Min Young Cho, Young-Kook Lee, Myoungho Pyo, Woon Bae Park, Kee-Sun Sohn

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OpenAlex topics: Advanced Battery Materials and Technologies · Perovskite Materials and Applications · Advancements in Battery Materials

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References (100+)

Show source-provided references
1. Perovskite to antiperovskite in abf3 compounds10.1080/00150199008223849 ↗
2. Minerals with antiperovskite structure: a review10.1524/zkri.2008.0008 ↗
3. Mn-based antiperovskite functional materials: review of research10.1088/1674-1056/22/6/067501 ↗
4. Research progress on Ni-based antiperovskite compounds
5. Alkaline-earth metal nitrides of the main-group elements: crystal structures and properties of inverse perovskites10.1002/zaac.201300063 ↗
6. Predicted nitrides with an antiperovskite structure10.1023/B:JORY.0000029831.93738.b1 ↗
7. Antiperovskite electrolytes for solid-state batteries10.1021/acs.chemrev.1c00594 ↗
8. Superionic conductivity in lithium-rich anti-perovskites10.1021/ja305709z ↗
9. Design principles for solid-state lithium superionic conductors10.1038/nmat4369 ↗
10. Novel Li3ClO based glasses with superionic properties for lithium batteries10.1039/C3TA15087A ↗
11. Rotational cluster anion enabling superionic conductivity in sodium-rich antiperovskite Na3OBH410.1021/jacs.9b01746 ↗
12. Anti-perovskites for solid-state batteries: recent developments, current challenges and future prospects10.1039/D1TA03680G ↗
13. Die Gesetze der Krystallochemie10.1007/BF01507527 ↗
14. Formability of ABO3 perovskites10.1016/j.jallcom.2003.10.017 ↗
15. On the application of the tolerance factor to inorganic and hybrid halide perovskites: a revised system10.1039/C5SC04845A ↗
16. Formability of ABX3 (X = F, Cl, Br, I) halide perovskites10.1107/S0108768108032734 ↗
17. Halide sublattice dynamics drive Li-ion transport in antiperovskites10.1039/D2TA02598A ↗
18. Theoretical formulation of Na3AO4X (A = S/Se, X = F/Cl) as high-performance solid electrolytes for all-solid-state sodium batteries10.1039/C9TA08584J ↗
19. Stability, elastic properties, and the Li transport mechanism of the protonated and fluorinated antiperovskite lithium conductors10.1021/acsami.0c17975 ↗
20. Sodium superionic conductors based on clusters10.1021/acsami.8b19003 ↗
21. Unlocking the chemical space in anti-perovskite conductors by incorporating anion rotation dynamics
22. Li-rich antiperovskite superionic conductors based on cluster ions10.1073/pnas.1704086114 ↗
23. Enhanced ionic conductivity of protonated antiperovskites via tuning lattice and rotational dynamics10.1039/D2TA08307H ↗
24. First-principles DFT Study on inverse Ruddlesden-Popper tetragonal compounds as solid electrolytes for all-solid-state Li+-ion batteries10.1021/acs.chemmater.1c00124 ↗
25. High-throughput screening of magnetic antiperovskites10.1021/acs.chemmater.8b01618 ↗
26. Accelerated workflow for antiperovskite-based solid state electrolytes10.1002/batt.202300204 ↗
27. Computational design of antiperovskite solid electrolytes10.1021/acs.jpcc.3c04953 ↗
28. Generalized neural-network representation of high-dimensional potential-energy surfaces10.1103/PhysRevLett.98.146401 ↗
29. Applying Classical, ab Initio, and machine-learning molecular dynamics simulations to the liquid electrolyte for rechargeable batteries10.1021/acs.chemrev.1c00904 ↗
30. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation10.1002/wcms.1581 ↗
31. Metal vacancy ordering in an antiperovskite resulting in two modifications of Fe2SeO10.1002/ange.201603920 ↗
32. Anti-Perovskite Li-Battery cathode materials10.1021/jacs.7b04444 ↗
33. Design of high-performance lead-free quaternary antiperovskites for photovoltaics via ion type inversion and anion ordering10.1021/jacs.1c06403 ↗
34. Theoretical design of double anti-perovskite Na6SOI2 as a super-fast ion conductor for solid Na+ ion batteries10.1039/C8TA08412B ↗
35. From anti-perovskite to double anti-perovskite: tuning lattice chemistry to achieve super-fast Li+ transport in cubic solid lithium halogen-chalcogenides10.1039/C7TA08698A ↗
36. Theoretical tuning of Ruddlesden-Popper type anti-perovskite phases as superb ion conductors and cathodes for solid sodium ion batteries10.1039/C9TA02166C ↗
37. Discovery of Pb-free hybrid organic–inorganic 2D perovskites using a stepwise optimization strategy10.1038/s41524-022-00781-z ↗
38. Discovery of a quaternary sulfide, Ba2- xLiAlS4:eu2+, and its potential as a fast-decaying LED phosphor10.1021/acs.chemmater.0c02243 ↗
39. Metaheuristics-assisted combinatorial screening of Eu2+-doped Ca-Sr-Ba-Li-Mg-Al-Si-Ge-N compositional space in search of a narrow-band green emitting phosphor and density functional theory calculations10.1021/acs.inorgchem.7b01341 ↗
40. Multi-variable bayesian optimization for a new composition with high Na+conductivity in the Na3PS4family10.1039/D1TA09886A ↗
41. Argyrodite configuration determination for DFT and AIMD calculations using an integrated optimization strategy10.1039/D2RA05889H ↗
42. Bremermann · 1958
43. Holland · 1992
44. J. Kennedy, R. Eberhart, Particle Swarm Optimization, in: Proceedings of ICNN'95 – international conference on neural networks, perth, WA, Australia, 27 November 1995 - 01 December 1995. 10.1109/ICNN.1995.488968.
45. A new heuristic optimization algorithm: harmony search
46. Cuckoo search via lévy flights
47. The bayesian approach to global optimization theory and applications
48. On-the-fly closed-loop materials discovery via Bayesian active learning10.1038/s41467-020-19597-w ↗
49. Settles · 2012
50. J. Snoek, H. Larochelle, R.P. Adams, Practical bayesian optimization of machine learning algorithms, (2012). arXiv:1206.2944.
51. K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, E. Xing, Neural architecture search with Bayesian optimisation and optimal transport, (2018). arXiv:1802.07191.
52. Automatic chemical design using a data-driven continuous representation of molecules10.1021/acscentsci.7b00572 ↗
53. Active learning with support vector machines in the drug discovery process10.1021/ci025620t ↗
54. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution10.1038/s41929-018-0142-1 ↗
55. Less is more: sampling chemical space with active learning
56. Query efficient posterior estimation in scientific experiments via bayesian active learning10.1016/j.artint.2016.11.002 ↗
57. Accelerated discovery of CO2 electrocatalysts using active machine learning10.1038/s41586-020-2242-8 ↗
58. Bayesian optimization for chemical products and functional materials10.1016/j.coche.2021.100728 ↗
59. Discovery of energy storage molecular materials using quantum chemistry-guided multiobjective bayesian optimization10.1021/acs.chemmater.1c02040 ↗
60. Cost-effective materials discovery: bayesian optimization across multiple information sources10.1039/D0MH00062K ↗
61. Bayesian optimization for material discovery processes with noise10.1039/D1ME00154J ↗
62. Bayesian optimization as a sustainable strategy for early-stage process development? a case study of Cu-catalyzed C-N coupling of sterically hindered pyrazines10.1021/acssuschemeng.3c02455 ↗
63. Query & Committee
64. Scaling deep learning for materials discovery
65. Fundamental and combinatorial approaches in the search for and optimisation of catalytic materials for the oxidative dehydrogenation of propane to propene10.1016/S0920-5861(01)00329-7 ↗
66. An evolutionary approach in the combinatorial selection and optimization of catalytic materials10.1016/S0926-860X(00)00643-8 ↗
67. A search for new red phosphors using a computational evolutionary optimization process10.1002/adma.200305291 ↗
68. Genetic algorithm-assisted optimization of partially dyed-TiO2 for room-temperature printable photoanodes of dye-sensitized solar cells10.1039/C1JM14416B ↗
69. A multi-element doping design for a high-performance LiMnPO4 cathode: via metaheuristic computation10.1039/C6TA10228J ↗
70. Generalized Gradient Approximation Made Simple10.1103/PhysRevLett.77.3865 ↗
71. Ab initio molecular dynamics for open-shell transition metals10.1103/PhysRevB.48.13115 ↗
72. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium10.1103/PhysRevB.49.14251 ↗
73. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set10.1016/0927-0256(96)00008-0 ↗
74. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set10.1103/PhysRevB.54.11169 ↗
75. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu10.1063/1.3382344 ↗
76. Projector augmented-wave method10.1103/PhysRevB.50.17953 ↗
77. From ultrasoft pseudopotentials to the projector augmented-wave method10.1103/PhysRevB.59.1758 ↗
78. Commentary: the materials project: a materials genome approach to accelerating materials innovation10.1063/1.4812323 ↗
79. Learning properties of ordered and disordered materials from multi-fidelity data10.1038/s43588-020-00002-x ↗
80. Discovery of lead-free hybrid organic/inorganic perovskites using metaheuristic-driven DFT calculations10.1021/acs.chemmater.0c04499 ↗
81. Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces10.1103/PhysRevLett.114.096405 ↗
82. Data-driven first-principles methods for the study and design of alkali superionic conductors10.1021/acs.chemmater.6b02648 ↗
83. Statology, Understanding the Standard Error of a Regression Slope, https://www.statology.org/standard-error-of-regression-slope (accessed 13 March 2024).
84. Statistical variances of diffusional properties from ab initio molecular dynamics simulations10.1038/s41524-018-0074-y ↗
85. GitHub: socoolblue/optimization, https://github.com/socoolblue/optimization 2022 (accessed 13 March 2024).
86. Bayesian optimization with adaptive surrogate models for automated experimental design10.1038/s41524-021-00662-x ↗
88. Multivariate adaptive regression splines
89. BART: bayesian additive regression trees10.1214/09-AOAS285 ↗
90. Li6PS5X: a class of crystalline Li-rich solids with an unusually high Li+ mobility10.1002/anie.200703900 ↗
91. Influence of lattice polarizability on the ionic conductivity in the lithium superionic argyrodites Li6PS5X (X = Cl, Br, I)10.1021/jacs.7b06327 ↗
92. Boosting solid-state diffusivity and conductivity in lithium superionic argyrodites by halide substitution10.1002/anie.201814222 ↗
93. Influence of aliovalent cation substitution and mechanical compression on Li-ion conductivity and diffusivity in argyrodite solid electrolytes10.1021/acs.chemmater.0c03090 ↗
94. E. Brochu, V.M. Cora, N. de Freitas, A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning, (2010) arXiv preprint: arXiv:1012.2599.
95. Evaluation of catalyst library optimization algorithms: comparison of the holographic research strategy and the genetic algorithm in virtual catalytic experiments10.1016/j.apcata.2006.01.028 ↗
96. Development of catalyst libraries for total oxidation of methane: a case study for combined application of “holographic research strategy and artificial neural networks” in catalyst library design10.1016/j.apcata.2005.02.019 ↗
97. Information mining using artificial neural networks and “holographic research strategy10.1016/S0926-860X(03)00285-0 ↗
98. Holographic research strategy for catalyst library design: description of a new powerful optimisation method10.1016/S0920-5861(03)00150-0 ↗
99. A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys10.1038/s41598-021-90237-z ↗
100. Physical content of the exact kohn-sham orbital energies: band gaps and derivative discontinuities10.1103/PhysRevLett.51.1884 ↗