Quantum-assisted machine learning screening for sustainable anode discovery in lithium-ion batteries

Fronzi, M; Stampfl, C; Ellis, A; Goudeli, E

Quantum-assisted machine learning screening for sustainable anode discovery in lithium-ion batteries

Fronzi, M

Stampfl, C Ellis, A Goudeli, E

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Journal of Power Sources, 2025, 652
Issue Date:: 2025-10-01

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Field	Value	Language
dc.contributor.author	Fronzi, M https://orcid.org/0000-0001-7855-9216
dc.contributor.author	Stampfl, C
dc.contributor.author	Ellis, A
dc.contributor.author	Goudeli, E
dc.date.accessioned	2026-02-16T02:14:27Z
dc.date.available	2026-02-16T02:14:27Z
dc.date.issued	2025-10-01
dc.identifier.citation	Journal of Power Sources, 2025, 652
dc.identifier.issn	0378-7753
dc.identifier.issn	1873-2755
dc.identifier.uri	http://hdl.handle.net/10453/193518
dc.description.abstract	A comprehensive analysis of 9835 crystal structures, 211 of which are calculated to be thermodynamically stable, is presented, assessing their potential as anode materials for lithium-ion batteries. Density functional theory (DFT) calculations and advanced machine learning techniques are employed to explore the stability, lithium diffusion, bulk modulus and shear stress, along with the relationships between atomic orbital overlap, energy density, and ion mobility, which is a crucial factors for rapid charging capabilities. The study also examines the combined effects of elemental composition and crystallographic space groups to identify the key drivers of structural toughness. A number of crystal structures are identified as promising anode materials, with some standing out for their exceptional stability and efficient lithium-ion mobility. These materials demonstrate significant potential for high-capacity, durable battery anodes, highlighting the importance of a multidimensional approach in battery material development. These insights provide a novel perspective on the interplay between physical, chemical, and electronic properties in optimising anode materials. This work offers valuable guidance for the future design and development of high-performance lithium-ion batteries, contributing to a more sustainable economy.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Journal of Power Sources
dc.relation.isbasedon	10.1016/j.jpowsour.2025.237347
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	03 Chemical Sciences, 09 Engineering
dc.subject.classification	Energy
dc.subject.classification	34 Chemical sciences
dc.subject.classification	40 Engineering
dc.title	Quantum-assisted machine learning screening for sustainable anode discovery in lithium-ion batteries
dc.type	Journal Article
utslib.citation.volume	652
utslib.for	03 Chemical Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-02-16T02:14:26Z
pubs.publication-status	Published
pubs.volume	652

Abstract:

A comprehensive analysis of 9835 crystal structures, 211 of which are calculated to be thermodynamically stable, is presented, assessing their potential as anode materials for lithium-ion batteries. Density functional theory (DFT) calculations and advanced machine learning techniques are employed to explore the stability, lithium diffusion, bulk modulus and shear stress, along with the relationships between atomic orbital overlap, energy density, and ion mobility, which is a crucial factors for rapid charging capabilities. The study also examines the combined effects of elemental composition and crystallographic space groups to identify the key drivers of structural toughness. A number of crystal structures are identified as promising anode materials, with some standing out for their exceptional stability and efficient lithium-ion mobility. These materials demonstrate significant potential for high-capacity, durable battery anodes, highlighting the importance of a multidimensional approach in battery material development. These insights provide a novel perspective on the interplay between physical, chemical, and electronic properties in optimising anode materials. This work offers valuable guidance for the future design and development of high-performance lithium-ion batteries, contributing to a more sustainable economy.

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http://hdl.handle.net/10453/193518