Neural-network parametrization of fundamental measure theory: From hard spheres to Lennard-Jones fluids.

Feugmo CGT

DOI: 10.1103/m7vz-r1qq
Published: 2026 Aug
Container: Physical review. E
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1103/m7vz-r1qq,
  title = {Neural-network parametrization of fundamental measure theory: From hard spheres to Lennard-Jones fluids.},
  author = {Feugmo CGT},
  year = {2026},
  journal = {Physical review. E},
  doi = {10.1103/m7vz-r1qq},
  url = {https://doi.org/10.1103/m7vz-r1qq}
}

RIS

TY  - JOUR
TI  - Neural-network parametrization of fundamental measure theory: From hard spheres to Lennard-Jones fluids.
AU  - Feugmo CGT
PY  - 2026
JO  - Physical review. E
DO  - 10.1103/m7vz-r1qq
UR  - https://doi.org/10.1103/m7vz-r1qq
ER  -

APA

CGT, F. (2026). Neural-network parametrization of fundamental measure theory: From hard spheres to Lennard-Jones fluids.. Physical review. E. https://doi.org/10.1103/m7vz-r1qq

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pubmed · retrieved 2026-09-25T12:36:35.775Z