A Mixed-Precision Approach to a Preconditioned Eigensolver for Efficient Density Functional Calculations on AI-Focused GPUs

Jeheon Woo, Sunghwan Choi

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DOI: 10.1021/acs.jctc.5c01800
Published: 2026-02-11
Container: Journal of Chemical Theory and Computation
Publisher: American Chemical Society (ACS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1021/acs.jctc.5c01800,
  title = {A Mixed-Precision Approach to a Preconditioned Eigensolver for Efficient Density Functional Calculations on AI-Focused GPUs},
  author = {Jeheon Woo and Sunghwan Choi},
  year = {2026},
  journal = {Journal of Chemical Theory and Computation},
  doi = {10.1021/acs.jctc.5c01800},
  url = {https://doi.org/10.1021/acs.jctc.5c01800}
}

RIS

TY  - JOUR
TI  - A Mixed-Precision Approach to a Preconditioned Eigensolver for Efficient Density Functional Calculations on AI-Focused GPUs
AU  - Jeheon Woo
AU  - Sunghwan Choi
PY  - 2026
JO  - Journal of Chemical Theory and Computation
DO  - 10.1021/acs.jctc.5c01800
UR  - https://doi.org/10.1021/acs.jctc.5c01800
ER  -

APA

Woo, J., & Choi, S. (2026). A Mixed-Precision Approach to a Preconditioned Eigensolver for Efficient Density Functional Calculations on AI-Focused GPUs. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.5c01800

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crossref · retrieved 2026-09-25T05:52:18.680Z