Tensor cluster expansion as an efficient surrogate for multi-ACE
Abstract
In this work, we generalize an on-lattice tensor cluster expansion (TCE) framework to an arbitrary correlation order. We show that these generalized tensor cluster expansion features span a subspace of MACE features, suggesting that TCE features can sufficiently pre-screen MACE features for chemical diversity in alloyed transition metal dichalcogenide (TMD) monolayers. We show that, using maximum-entropy subset sampling, we can empirically recover chemical diversity from MACE features using a cheaper TCE representation. We then fine-tune a multi-ACE (MACE) potential with both subsets, and use this potential to predict temperature-dependent vibrational properties in alloyed TMD monolayers with varying chemical ordering. In our approach, TCE features can be used as cheap surrogate features for pre-screening chemically rich configurations using MACE features, allowing for the more efficient finetuning of atomic cluster expansion (ACE) potentials, thus achieving a tractable prediction of finite-temperature alloy thermodynamics on modest hardware.
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BibTeX
@misc{jeffries2026tensor,
title = {{Tensor cluster expansion as an efficient surrogate for multi-ACE}},
author = {Jacob Jeffries and Enrique Martinez and Eva Zarkadoula},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.41969},
eprint = {2610.41969},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.41969},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Tensor cluster expansion as an efficient surrogate for multi-ACE
AU - Jacob Jeffries
AU - Enrique Martinez
AU - Eva Zarkadoula
PY - 2026
DA - 2026/10/30
PB - arXiv
DO - 10.48550/arxiv.2610.41969
UR - https://arxiv.org/abs/2610.41969
AB - In this work, we generalize an on-lattice tensor cluster expansion (TCE) framework to an arbitrary correlation order. We show that these generalized tensor cluster expansion features span a subspace of MACE features, suggesting that TCE features can sufficiently pre-screen MACE features for chemical diversity in alloyed transition metal dichalcogenide (TMD) monolayers. We show that, using maximum-entropy subset sampling, we can empirically recover chemical diversity from MACE features using a cheaper TCE representation. We then fine-tune a multi-ACE (MACE) potential with both subsets, and use this potential to predict temperature-dependent vibrational properties in alloyed TMD monolayers with varying chemical ordering. In our approach, TCE features can be used as cheap surrogate features for pre-screening chemically rich configurations using MACE features, allowing for the more efficient finetuning of atomic cluster expansion (ACE) potentials, thus achieving a tractable prediction of finite-temperature alloy thermodynamics on modest hardware.
ER -