Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics

Miyagawa, Takeru; Krishnan, Namita; Grumet, Manuel; Baecker, Christian Reverón; Kaiser, Waldemar; Egger, David A.

doi:10.1039/d4ta00452c

J. Mater. Chem. A

2024

DOI: 10.1039/d4ta00452c

|View full text |Cite

Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics

Takeru Miyagawa,

Namita Krishnan,

Manuel Grumet

et al.

Abstract: Solid-state ion conductors (SSICs) have emerged as a promising material class for electrochemical storage devices and novel compounds of this kind are continuously being discovered. High-throughout approaches that enable a...

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 142 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors

O’Leary,

Grumet,

Kaiser

et al. 2024

J. Am. Chem. Soc.

View full text Add to dashboard Cite

The successful design of solid-state photo-and electrochemical devices depends on the careful engineering of point defects in solid-state ion conductors. Characterization of point defects is critical to these efforts, but the best-developed techniques are difficult and timeconsuming. Raman spectroscopy�with its exceptional speed, flexibility, and accessibility�is a promising alternative. Raman signatures arise from point defects due to local symmetry breaking and structural distortions. Unfortunately, the assignment of these signatures is often hampered by a shortage of reference compounds and corresponding reference spectra. This issue can be circumvented by calculation of defect-induced Raman signatures from first principles, but this is computationally demanding. Here, we introduce an efficient computational procedure for the prediction of point defect Raman signatures in solid-state ion conductors. Our method leverages machinelearning force fields and "atomic Raman tensors", i.e., polarizability fluctuations due to motions of individual atoms. We find that our procedure reduces computational cost by up to 80% compared to existing first-principles frozen-phonon approaches. These efficiency gains enable synergistic computational−experimental investigations, in our case allowing us to precisely interpret the Raman spectra of Sr(Ti 0.94 Ni 0.06 )O 3-δ , a model oxygen ion conductor. By predicting Raman signatures of specific point defects, we determine the nature of dominant defects and unravel impacts of temperature and quenching on in situ and ex situ Raman spectra. Specifically, our findings reveal the temperature-dependent distribution and association behavior of oxygen vacancies and nickel substitutional defects. Overall, our approach enables rapid Raman-based characterization of point defects to support defect engineering in novel solid-state ion conductors.

show abstract

Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors

O’Leary,

Grumet,

Kaiser

et al. 2024

J. Am. Chem. Soc.

View full text Add to dashboard Cite

show abstract

Thermal conductivity of Li3PS4 solid electrolytes with ab initio accuracy

Tisi,

Grasselli,

Gigli

et al. 2024

Phys. Rev. Materials

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics

Abstract: Solid-state ion conductors (SSICs) have emerged as a promising material class for electrochemical storage devices and novel compounds of this kind are continuously being discovered. High-throughout approaches that enable a...

Cited by 2 publications

References 142 publications

Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors

Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors

Thermal conductivity of Li3PS4 solid electrolytes with ab initio accuracy

Contact Info

Product

Resources

About