Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states.

Cho Y, Briling KR, Calvino Alonso Y, Laplaza R, Corminboeuf C

DOI: 10.1039/d5dd00571j
Published: 2026 May 20
Container: Digital discovery
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1039/d5dd00571j,
  title = {Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states.},
  author = {Cho Y and Briling KR and Calvino Alonso Y and Laplaza R and Corminboeuf C},
  year = {2026},
  journal = {Digital discovery},
  doi = {10.1039/d5dd00571j},
  url = {https://doi.org/10.1039/d5dd00571j}
}

RIS

TY  - JOUR
TI  - Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states.
AU  - Cho Y
AU  - Briling KR
AU  - Calvino Alonso Y
AU  - Laplaza R
AU  - Corminboeuf C
PY  - 2026
JO  - Digital discovery
DO  - 10.1039/d5dd00571j
UR  - https://doi.org/10.1039/d5dd00571j
ER  -

APA

Y, C., KR, B., Y, C. A., R, L., & C, C. (2026). Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states.. Digital discovery. https://doi.org/10.1039/d5dd00571j

Source records

pubmed · retrieved 2026-09-25T05:16:53.049Z
europe-pmc · retrieved 2026-09-25T05:16:53.084Z