Enabling Domain-Specific Atomistic Models: A Machine Learning Potential for the Solid Acid Family

Abstract

Machine-learned interatomic potentials trained across the periodic table have made atomistic simulation broadly accessible, and specializing them to a single compound class is widely expected to improve accuracy. Yet examples remain scarce, and fewer still surpass universal models in speed or reach a higher level of electronic-structure theory. Here we present a potential that is universal within the class of water-free solid-state hydrogen-bond network mediated proton conductors rather than across chemistry and a database of 4.4 million first-principles configurations spanning 55 materials. It surpasses leading general-purpose potentials across this domain, recovering measured activation energies and the ordering of anion rotational dynamics that those models miss; agreement on static structure does not imply agreement on transport. Accuracy falls for compositions far from the training set but stays competitive for close structural relatives, and a higher level of electronic-structure theory is reached with a few hundred additional configurations per material. We further introduce a compact variant carrying a fifth of the parameters, faster still and yet more accurate than every general-purpose model tested. It sustains more than a quarter of a million atoms on a single graphics processor, placing grain boundaries and the transition into the highly conducting phase within reach, and making a quantum treatment of the protons affordable. Reference accuracy and accessible system size thus become largely independent, offering a template for other compound classes.

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BibTeX RIS

BibTeX

@misc{hanseroth2026enabling,
  title = {{Enabling Domain-Specific Atomistic Models: A Machine Learning Potential for the Solid Acid Family}},
  author = {Jonas Hänseroth and Rose Asuka Baroness von Stackelberg and Christian Dreßler},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.38531},
  eprint = {2609.38531},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.38531},
}

RIS

TY  - UNPB
TI  - Enabling Domain-Specific Atomistic Models: A Machine Learning Potential for the Solid Acid Family
AU  - Jonas Hänseroth
AU  - Rose Asuka Baroness von Stackelberg
AU  - Christian Dreßler
PY  - 2026
DA  - 2026/09/29
PB  - arXiv
DO  - 10.48550/arxiv.2609.38531
UR  - https://arxiv.org/abs/2609.38531
AB  - Machine-learned interatomic potentials trained across the periodic table have made atomistic simulation broadly accessible, and specializing them to a single compound class is widely expected to improve accuracy. Yet examples remain scarce, and fewer still surpass universal models in speed or reach a higher level of electronic-structure theory. Here we present a potential that is universal within the class of water-free solid-state hydrogen-bond network mediated proton conductors rather than across chemistry and a database of 4.4 million first-principles configurations spanning 55 materials. It surpasses leading general-purpose potentials across this domain, recovering measured activation energies and the ordering of anion rotational dynamics that those models miss; agreement on static structure does not imply agreement on transport. Accuracy falls for compositions far from the training set but stays competitive for close structural relatives, and a higher level of electronic-structure theory is reached with a few hundred additional configurations per material. We further introduce a compact variant carrying a fifth of the parameters, faster still and yet more accurate than every general-purpose model tested. It sustains more than a quarter of a million atoms on a single graphics processor, placing grain boundaries and the transition into the highly conducting phase within reach, and making a quantum treatment of the protons affordable. Reference accuracy and accessible system size thus become largely independent, offering a template for other compound classes.
ER  -

Abstract

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