Quantitative structure-property relationship study of cathode volume changes in lithium ion batteries using ab-initio and partial least squares analysis
Source abstract
Abstract
In this paper, we report a method through the combination of ab-initio calculations and partial least squares (PLS) analysis to develop the Quantitative Structure –Activity Relationship (QSAR) formulations of cathode volume changes in lithium ion batteries. The PLS analysis is based on ab-initio calculation data of 14 oxide cathodes with spinel structure LiX2O4 and 14 oxide cathodes with layered-structure LiXO2 (X = Ti, V, Cr, Mn, Fe, Co, Ni, Nb, Mo, Ru, Rh, Pd, Ta, Ir). Five types of descriptors, describing the characteristics of each compound from crystal structure, element, composition, local distortion and electronic level, with 34 factors in total, are adopted to obtain the QSAR formulation. According to the variable importance in projection analysis, the radius of X4+ ion, and the X octahedron descriptors make major contributions to the volume change of cathode during delithiation. The analysis is hopefully applied to the virtual screening and combinatorial design of low-strain cathode materials for lithium ion batteries.
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Research context
OpenAlex topics: Advancements in Battery Materials · Machine Learning in Materials Science · X-ray Diffraction in Crystallography
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (36)
Show source-provided references
1. Contribution of Li-ion batteries to the environmental impact of electric vehicles10.1021/es903729a ↗
2. Energy storage devices for future hybrid electric vehicles10.1016/j.jpowsour.2006.10.090 ↗
3. Electrical energy storage for the grid: a battery of choices10.1126/science.1212741 ↗
4. Issues and challenges facing rechargeable lithium batteries10.1038/35104644 ↗
5. Understanding volume change in lithium-ion cells during charging and discharging using in situ measurements10.1149/1.2386933 ↗
6. A high capacity nano-Si composite anode material for lithium rechargeable batteries10.1149/1.1390899 ↗
7. From Ab initio calculations to multiscale design of Si/C core-shell particles for Li-ion anodes10.1021/nl500410g ↗
8. Lithium alloy negative electrodes10.1016/S0378-7753(99)00124-X ↗
9. Large-volume-change electrodes for Li-ion batteries of amorphous alloy particles held by elastomeric tethers10.1016/j.elecom.2003.08.017 ↗
10. Porous Li4Ti5O12 coated with N-doped carbon from ionic liquids for Li-ion batteries10.1002/adma.201003294 ↗
11. Zero-strain insertion material of LiLi1/3Ti5/3O4 for rechargeable lithium cells10.1149/1.2048592 ↗
12. A zero-strain layered metal oxide as the negative electrode for long-life sodium-ion batteries10.1038/ncomms3365 ↗
13. A highly reversible, low-strain Mg-ion insertion anode material for rechargeable Mg-ion batteries10.1038/am.2014.61 ↗
14. A general-purpose machine learning framework for predicting properties of inorganic materials10.1038/npjcompumats.2016.28 ↗
15. Machine learning in materials science: recent progress and emerging applications
16. Big data of materials science: critical role of the descriptor10.1103/PhysRevLett.114.105503 ↗
17. Fast and accurate modeling of molecular atomization energies with machine learning10.1103/PhysRevLett.108.058301 ↗
18. An implementation of artificial neural-network potentials for atomistic materials simulations: performance for TiO210.1016/j.commatsci.2015.11.047 ↗
19. Finding Nature's missing ternary oxide compounds using machine learning and density functional theory10.1021/cm100795d ↗
20. New opportunities for materials informatics: resources and data mining techniques for uncovering hidden relationship10.1557/jmr.2016.80 ↗
21. Structural and electrical characterization of Li(Mn1-δTiδ)2O4 electrode materials10.1016/S0167-2738(98)00365-8 ↗
22. Die Strukturen von LiVO2, NaVO2, LiCrO2 und NaCrO210.1515/znb-1954-0911 ↗
23. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set10.1016/0927-0256(96)00008-0 ↗
24. Projector augmented-wave method10.1103/PhysRevB.50.17953 ↗
25. Generalized gradient approximation made simple10.1103/PhysRevLett.77.3865 ↗
26. First-principles prediction of redox potentials in transition-metal compounds with LDA+U10.1103/PhysRevB.70.235121 ↗
27. 1989
28. From bond valence maps to energy landscapes for mobile ions in ion-conducting solids10.1016/j.ssi.2006.03.054 ↗
29. High power lithium ion battery materials by computational design10.1002/pssa.201001116 ↗
30. High-throughput design and optimization of fast lithium ion conductors by the combination of bond-valence method and density functional theory10.1038/srep14227 ↗
31. Candidate structures for inorganic lithium solid-state electrolytes identified by high-throughput bond-valence calculations10.1016/j.jmat.2015.08.001 ↗
32. The geometry of polyhedral distortions10.1107/S0567740874004560 ↗
33. Distortion parameters for coordination polyhedral10.1180/minmag.1976.040.313.18 ↗
34. Quadratic elongation: a quantitative measure of distortion in coordination polyhedra10.1126/science.172.3983.567 ↗
35. PLS path modeling10.1016/j.csda.2004.03.005 ↗
36. PLS regression and statistical models