Machine-learned interatomic potentials for battery materials: from fundamental methodology to emerging applications in electrodes, electrolytes, and interfaces
- DOI
- 10.1039/d6cp01595f
- Published
- 2026
- Container
- Physical Chemistry Chemical Physics
- Publisher
- Royal Society of Chemistry (RSC)
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1039/d6cp01595f,
title = {Machine-learned interatomic potentials for battery materials: from fundamental methodology to emerging applications in electrodes, electrolytes, and interfaces},
author = {Keisuke Makino and Teruyuki Kato and Sayato Terashima and Yoshiya Matsuoka and So Takamoto and Chikashi Shinagawa and Yusuke Asano and Masanobu Nakayama},
year = {2026},
journal = {Physical Chemistry Chemical Physics},
doi = {10.1039/d6cp01595f},
url = {https://doi.org/10.1039/d6cp01595f}
}RIS
TY - JOUR TI - Machine-learned interatomic potentials for battery materials: from fundamental methodology to emerging applications in electrodes, electrolytes, and interfaces AU - Keisuke Makino AU - Teruyuki Kato AU - Sayato Terashima AU - Yoshiya Matsuoka AU - So Takamoto AU - Chikashi Shinagawa AU - Yusuke Asano AU - Masanobu Nakayama PY - 2026 JO - Physical Chemistry Chemical Physics DO - 10.1039/d6cp01595f UR - https://doi.org/10.1039/d6cp01595f ER -
APA
Makino, K., Kato, T., Terashima, S., Matsuoka, Y., Takamoto, S., Shinagawa, C., Asano, Y., & Nakayama, M. (2026). Machine-learned interatomic potentials for battery materials: from fundamental methodology to emerging applications in electrodes, electrolytes, and interfaces. Physical Chemistry Chemical Physics. https://doi.org/10.1039/d6cp01595f
Source records
- crossref · retrieved 2026-09-25T02:09:06.117Z