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A Case Study of an Energy Barrier in Li-Ion Battery Cathode Material Using DFT and Post-HF Approaches

Articolo
Data di Pubblicazione:
2024
Abstract:
With the ever-increasing interest toward energy storage materials, an accurate understanding of the underlying physicochemical processes becomes mandatory for enabling accurate and predictive simulations. In this study, we apply multilevel quantum chemical calculations on a benchmark material commonly adopted as a cathode in Lithium batteries, Li0.5Co0.5+3Ni0.5+4O2. We estimate the Lithium hopping barrier, a key quantity for the estimate of Li diffusion coefficient, at different levels: Hartree-Fock (HF), density functional theory (DFT), periodic local Møller-Plesset perturbation theory of second order, complemented with a coupled cluster correction evaluated using an embedded-fragment approach. The post-HF methods were used here not only for benchmarking the key quantities themselves but also for assessing the accuracy of different functionals and probing the influence of the long-range and static correlation. For the given system and quantity in question, we observe that obtained results do not significantly vary across different DFT functionals or post-HF methods, which is rather uncommon. Such an agreement between the employed methods suggests that static correlation, even if prominent in this system, cancels out in the studied energy differences. In fact, the values of the T1 diagnostics, which test the reliability of the single-reference description, do vary from one fragment to another. But for certain fragments they are fairly small and of similar magnitude, indicating the applicability of such fragments for the correction. Our best estimate of the reaction barrier is about 0.85 eV.
Tipologia CRIS:
03A-Articolo su Rivista
Elenco autori:
Bonometti, Laura; Usvyat, Denis; Maschio, Lorenzo
Autori di Ateneo:
MASCHIO Lorenzo
Link alla scheda completa:
https://iris.unito.it/handle/2318/2068251
Pubblicato in:
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Journal
Progetto:
Future HPC & Big Data-finanziato con fondi PNRR MUR-M4C2-Investimento 1.4-Avviso"Centri Nazionali"-D.D.n.3138 del 16/12/2021 rettificato con DD n.3175 del 18/12/2021,codice MUR CN00000013, CUP D13C22001340001
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Settori (5)


PE3_3 - Transport properties of condensed matter - (2024)

PE4_13 - Theoretical and computational chemistry - (2024)

INFORMATICA, AUTOMAZIONE e INTELLIGENZA ARTIFICIALE - Informatica per la Chimica

PIANETA TERRA, AMBIENTE, CLIMA, ENERGIA e SOSTENIBILITA' - Energia e Fonti Energetiche

SCIENZE MATEMATICHE, CHIMICHE, FISICHE - Materiali Avanzati
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