Deep-learning based prediction of chemo-mechanics and damage in battery active materials
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OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Machine Learning in Materials Science
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References (56)
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1. High-energy lithium-ion batteries: recent progress and a promising future in applications10.1002/eem2.12450 ↗
2. High-nickel NMA: A cobalt-free alternative to NMC and NCA cathodes for lithium-ion batteries10.1002/adma.202002718 ↗
3. Synthesis of Sn-doped Li-rich NMC as a cathode material for Li-ion batteries10.1016/j.electacta.2022.141743 ↗
4. Application of electron backscatter diffraction techniques to quantify effects of aging on sub-grain and spatial heterogeneity in NMC cathodes10.1016/j.ensm.2021.10.018 ↗
5. Anisotropic mechanical properties of LiNixMnyCo1−x−yO2 cathodes for Li-ion batteries: A first-principles theoretical study10.1016/j.actamat.2024.119751 ↗
6. Cohesive phase-field chemo-mechanical simulations of inter-and trans-granular fractures in polycrystalline NMC cathodes via image-based 3D reconstruction10.1016/j.jpowsour.2024.234054 ↗
7. A length-scale insensitive cohesive phase-field interface model: Application to concurrent bulk and interface fracture simulation in lithium-ion battery materials10.1016/j.jmps.2024.106013 ↗
8. A chemo-mechanical grain boundary model and its application to understand the damage of Li-ion battery materials10.1016/j.scriptamat.2020.03.027 ↗
9. Direct observation of defect-aided structural evolution in a nickel-rich layered cathode10.1002/anie.202008144 ↗
10. Kinetic limitations in cycled nickel-rich NCM cathodes and their effect on the phase transformation behavior10.1021/acsaem.9b02483 ↗
11. Degradation model of high-nickel positive electrodes: Effects of loss of active material and cyclable lithium on capacity fade10.1016/j.jpowsour.2022.232461 ↗
12. Three-dimensional numerical simulations on the effect of particle porosity of lithium-nickel–manganese–cobalt–oxide on the performance of positive lithium-ion battery electrodes10.1002/ente.202000676 ↗
13. Modeling anisotropic transport in polycrystalline battery materials10.3390/batteries9060310 ↗
14. Fracture mechanisms of NCM polycrystalline particles in lithium-ion batteries: A review10.1016/j.est.2024.110807 ↗
15. Impact of mechanical degradation in polycrystalline NMC particle on the electrochemical performance of lithium-ion batteries10.1149/1945-7111/ad5622 ↗
16. Direct observations of electrochemically induced intergranular cracking in polycrystalline NMC811 particles10.1039/D3TA03057A ↗
17. Coupled electrochemical–mechanical model for fracture analysis in active materials of lithium ion batteries10.1016/j.jpowsour.2023.233378 ↗
18. Polycrystalline secondary particle size regulation boosts the cycle performance of ultra-high-nickel layered oxide cathode materials10.1039/9781837674589 ↗
19. Comparative study of polycrystalline and single-crystal NCM811 cathode materials: the role of crystal defects in electrochemical performance10.1039/D3TA03290F ↗
20. Direct measurement of gaussian distributed radial crystallographic orientations of polycrystalline, layered-oxide secondary particles and their impact on materials utilization in battery cathodes10.1016/j.ensm.2021.11.025 ↗
21. Evaluating the effects of grain anisotropy on the effective chemo-mechanical properties of secondary particles in lithium-ion batteries
22. Microstructure impact on chemo-mechanical fracture of polycrystalline lithium-ion battery cathode materials10.1016/j.engfracmech.2024.110370 ↗
23. Microstructural adjusting crack evolution of polycrystalline NCM particle during charge/discharge cycle10.1149/1945-7111/ac8ee3 ↗
24. Microstructure-chemomechanics relations of polycrystalline cathodes in solid-state batteries10.1016/j.eml.2024.102164 ↗
25. Data-driven multiscale simulation of solid-state batteries via machine learning10.1016/j.commatsci.2023.112186 ↗
26. Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework
27. Time-dependent deep learning predictions of 3D electrode particle-resolved microstructure effect on voltage discharge curves10.1016/j.jpowsour.2023.233087 ↗
28. Rapid prediction of particle-scale state-of-lithiation in Li-ion battery microstructures using convolutional neural networks10.1016/j.apenergy.2024.122803 ↗
29. Swift prediction of battery performance: Applying machine learning models on microstructural electrode images for lithium-ion batteries10.3390/batteries10030099 ↗
30. Homogenization-informed convolutional neural networks for estimation of li-ion battery effective properties10.1007/s11242-022-01862-9 ↗
31. Stress prediction of the particle structure of all-solid-state batteries by numerical simulation and machine learning10.3389/fceng.2022.836282 ↗
32. Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks
33. Fuchs · 2024
34. Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion10.1038/s42256-021-00322-1 ↗
35. End-to-end three-dimensional designing of complex disordered materials from limited data using machine learning10.1103/PhysRevE.106.055301 ↗
36. Large-scale synthesis of metal additively-manufactured microstructures using Markov random fields10.1016/j.commatsci.2022.111228 ↗
37. Polycrystalline microstructure reconstruction using Markov random fields and histogram matching10.1016/j.cad.2019.102806 ↗
38. Comparison and validation of stochastic microstructure characterization and reconstruction: Machine learning vs. deep learning methodologies10.1016/j.actamat.2024.120220 ↗
39. A quasi-physical method for random packing of spherical particles10.1016/j.powtec.2022.118002 ↗
40. Stratified sampling10.1007/978-94-017-1404-4_5 ↗
41. On the two different aspects of the representative method: the method of stratified sampling and the method of purposive selection
42. Percolation micro-model to predict the effective properties of the composite electrode with poly-dispersed particle sizes10.1016/j.jpowsour.2010.11.107 ↗
43. On the utility of lognormal distributions in the analysis of electrochemical systems10.1016/j.jelechem.2004.11.017 ↗
44. Simulating the impact of particle size distribution on the performance of graphite electrodes in lithium-ion batteries10.1002/ente.201600232 ↗
45. Cracking and associated volumetric expansion of NMC811 secondary particles10.1016/j.jpowsour.2023.233745 ↗
46. W-doping of dense NMC811 hydroxide through wet-impregnation and its impact on crystal structure, phase transition related gas evolution and electrochemical performance at elevated upper cut-off voltage10.1149/1945-7111/aca360 ↗
47. A review on modeling of electro-chemo-mechanics in lithium-ion batteries10.1016/j.jpowsour.2018.12.011 ↗
48. Deep learning-based inverse scattering with structural similarity loss functions10.1109/JSEN.2020.3030321 ↗
49. Scheduled sampling for sequence prediction with recurrent neural networks
50. Dropout as a bayesian approximation: Representing model uncertainty in deep learning
51. Microstructural thermal stresses in ceramic materials10.1016/0022-5096(88)90024-5 ↗
52. Quantification of microcrack characteristics and implications for stiffness and strength of granite10.1016/j.ijrmms.2017.10.013 ↗
53. Fracture testing of lithium-ion battery cathode secondary particles in-situ inside the scanning electron microscope10.1002/batt.202300032 ↗
54. In situ fracture behavior of single crystal LiNi0.8Mn0.1Co0.1O2 (NMC811)10.1002/batt.202400077 ↗
55. Heterogeneous damage in Li-ion batteries: Experimental analysis and theoretical modeling10.1016/j.jmps.2019.05.003 ↗
56. Fracture mechanisms of NCM polycrystalline particles in lithium-ion batteries: A review10.1016/j.est.2024.110807 ↗