Artificial intelligence and machine learning for targeted energy storage solutions
Source abstract
Abstract
Abstract With the application of machine learning to large-material data sets, models are being developed that allow us to better predict novel materials with designed properties. Advances in artificial intelligence and its subclasses, as well as compute infrastructure, are making it possible to rapidly compute material properties, to access time/length scales and chemical spaces beyond the current capabilities of density functional theory and to outperform humans in interpretation and characterization of the data. This review highlights the latest developments in the field with special interest to energy storage materials.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Machine Learning in Materials Science · Advancements in Battery Materials · Catalysis and Oxidation Reactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (61)
Show source-provided references
1. Materials discovery and design using machine learning
2. Revealing the spectrum of unknown layered materials with superhuman predictive abilities10.1021/acs.jpclett.8b03187 ↗
3. Self-consistent equations including exchange and correlation effects10.1103/PhysRev.140.A1133 ↗
4. Inhomogeneous electron gas10.1103/PhysRev.136.B864 ↗
5. Python materials genomics (pymatgen): a robust, open-source Python library for materials analysis10.1016/j.commatsci.2012.10.028 ↗
6. FireWorks: a dynamic workflow system designed for high-throughput applications10.1002/cpe.3505 ↗
7. Atomate: a high-level Interface to generate, execute, and analyze computational materials science workflows10.1016/j.commatsci.2017.07.030 ↗
8. Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations10.1039/c1jm12216a ↗
9. Phosphates as lithium-ion battery cathodes: an evaluation based on high-throughput ab initio calculations10.1021/cm200949v ↗
10. Phase stability, electrochemical stability and ionic conductivity of the Li 10±1 MP 2 X 12 (M = Ge, Si, Sn, Al or P, and X = O, S or Se) family of superionic conductors10.1039/C2EE23355J ↗
11. Oganov · 2010
12. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution10.1038/nmat1752 ↗
13. A search model for topological insulators with high-throughput robustness descriptors10.1038/nmat3332 ↗
14. The harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid10.1021/jz200866s ↗
15. Commentary: the materials project: a materials Genome approach to accelerating materials innovation10.1063/1.4812323 ↗
16. Flexible composite solid electrolyte facilitating highly stable “soft contacting” Li-electrolyte Interface for solid state lithium-ion batteries10.1002/aenm.201701437 ↗
17. Multi-scale computation methods: their applications in lithium-ion battery research and development10.1088/1674-1056/25/1/018212 ↗
18. Artificial intelligence—the revolution Hasn't happened yet
19. Machine learning-assisted discovery of solid Li-ion conducting materials10.1021/acs.chemmater.8b03272 ↗
20. Reviving lithium-metal anodes for next-generation high-energy batteries10.1002/adma.201700007 ↗
21. Redox mediators for Li-O 2 batteries: status and Perspectives10.1002/adma.201704162 ↗
22. Advances in lithium-containing anodes of aprotic Li-O 2 batteries: challenges and strategies for improvements10.1002/smtd.201700135 ↗
23. Advanced micro/nanostructures for lithium metal anodes10.1002/advs.201600445 ↗
24. Reviving the lithium metal anode for high-energy batteries10.1038/nnano.2017.16 ↗
25. Design principles for solid-state lithium superionic conductors10.1038/nmat4369 ↗
26. Lithium battery chemistries enabled by solid-state electrolytes10.1038/natrevmats.2016.103 ↗
27. Machine learning: trends, Perspectives, and prospects10.1126/science.aaa8415 ↗
28. Solid-state lithium–sulfur batteries operated at 37 °C with composites of nanostructured Li 7 La 3 Zr 2 O 12/carbon foam and polymer10.1021/acs.nanolett.7b00221 ↗
29. The pursuit of solid-state electrolytes for lithium batteries: from comprehensive insight to emerging horizons10.1039/C6MH00218H ↗
30. Data-driven health estimation and Lifetime prediction of lithium-ion batteries: a review10.1016/j.rser.2019.109254 ↗
31. Improving lithium–sulphur batteries through spatial control of sulphur species deposition on a hybrid electrode surface10.1038/ncomms4943 ↗
32. Properties of surface film on lithium anode with LiNO3 as lithium salt in electrolyte solution for lithium–sulfur batteries10.1016/j.electacta.2012.07.118 ↗
33. Recent advances in cathode materials for rechargeable lithium–sulfur batteries10.1039/C9NR04415A ↗
34. The matter simulation (R)evolution10.1021/acscentsci.7b00550 ↗
35. Jørgensen · 2018 · 1
36. Data-driven studies of Li-Ion-Battery materials10.3390/cryst9010054 ↗
37. Research progress on vanadium-based cathode materials for sodium ion batteries10.1039/C8TA01627E ↗
38. Combinatorial screening for new materials in unconstrained composition space with machine learning10.1103/PhysRevB.89.094104 ↗
39. Modeling and theoretical design of next-generation lithium metal batteries10.1016/j.ensm.2018.05.007 ↗
40. Hollow carbon nanofibers filled with MnO 2 nanosheets as efficient sulfur hosts for lithium-sulfur batteries10.1002/anie.201506972 ↗
41. Prussian blue-derived Fe2O3/sulfur composite cathode for lithium–sulfur batteries10.1016/j.matlet.2014.08.115 ↗
42. Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials10.1039/C6EE02697D ↗
43. Mastering the game of go without human knowledge10.1038/nature24270 ↗
44. The electrolyte Genome project: a big data approach in battery materials discovery10.1016/j.commatsci.2015.02.050 ↗
45. A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning10.1016/j.ensm.2019.06.011 ↗
46. The chemical space project10.1021/ar500432k ↗
47. Screening billions of candidates for solid lithium-ion conductors: a transfer learning approach for small data
48. Sanchez-Lengeling · 2019
49. Optimizing chemical reactions with deep reinforcement learning10.1021/acscentsci.7b00492 ↗
50. Guimaraes · 2017
51. Rational designs and engineering of hollow micro-/nanostructures as sulfur hosts for advanced lithium–sulfur batteries10.1039/C6EE02364A ↗
52. Interface and doping effects on Li ion storage behavior of graphene/Li 2 O10.1021/acs.jpcc.7b04642 ↗
53. Challenges and Perspectives for new material solutions in batteries10.1016/j.ssc.2019.113733 ↗
54. Aspuru-Guzik
55. Electrochemical performance enhancement of sodium-ion batteries fabricated with NaNi1/3Mn1/3Co1/3O2 cathodes using support vector regression-simplex algorithm approach
56. Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materials10.1038/s41598-018-34201-4 ↗
57. A new method for determining the concentration of electrolyte components in lithium-ion cells, using fourier transform infrared spectroscopy and machine learning10.1149/2.0861802jes ↗
58. Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries10.1039/C8RA07112H ↗
59. Predicting the electrochemical properties of lithium-ion battery electrode materials with the Quantum neural network algorithm10.1021/acs.jpcc.8b11335 ↗
60. Probabilistic prediction algorithm for cycle life of energy storage in lithium battery10.3390/wevj10010001 ↗
61. Gaussian process regression for in situ capacity estimation of lithium-ion batteries10.1109/TII.2018.2794997 ↗