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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2502.21179 |
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| _version_ | 1866910934222503936 |
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| author | Christiansen, Mads-Peter Verner Hammer, Bjørk |
| author_facet | Christiansen, Mads-Peter Verner Hammer, Bjørk |
| contents | Foundation models of interatomic potentials, so called universal potentials, may require fine-tuning or residual corrections when applied to specific subclasses of materials. In the present work, we demonstrate how such augmentation can be accomplished via $Δ$-learning based on the representation already embedded in the universal potentials. The $Δ$-model introduced is a Gaussian Process Regression (GPR) model and various types of aggregation (global, species-separated, and atomic) of the representation vector are discussed. Employing a specific universal potential, CHGNet [Deng et al., Nat. Mach. Intell. 5, 1031 (2023)], in a global structure optimization setting, we find that it correctly describes the energetics of the "8" Cu oxide, which is an ultra-thin oxide film on Cu(111). The universal potential model even predicts a more favorable structure compared to that discussed in recent DFT-based literature. Moving to sulfur adatom overlayers on Cu(111), Ag(111), and Au(111) the CHGNet model, however, requires corrections. We demonstrate that these are efficiently provided via the GPR-based $Δ$-model formulated on the CHGNet's own internal atomic embedding representation. The need for corrections is tracked to the scarcity of metal-sulfur atomic environments in the materials project database that CHGNet is trained on leading to an overreliance on sulfur-sulfur atomic environments. Other universal potentials trained on the same data, MACE-MP0, SevenNet-0, and ORB-v2-only-MPtrj show similar behavior, but with varying degrees of error, demonstrating the general need for augmentation schemes for universal potential models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21179 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $Δ$-model correction of Foundation Model based on the models own understanding Christiansen, Mads-Peter Verner Hammer, Bjørk Materials Science Foundation models of interatomic potentials, so called universal potentials, may require fine-tuning or residual corrections when applied to specific subclasses of materials. In the present work, we demonstrate how such augmentation can be accomplished via $Δ$-learning based on the representation already embedded in the universal potentials. The $Δ$-model introduced is a Gaussian Process Regression (GPR) model and various types of aggregation (global, species-separated, and atomic) of the representation vector are discussed. Employing a specific universal potential, CHGNet [Deng et al., Nat. Mach. Intell. 5, 1031 (2023)], in a global structure optimization setting, we find that it correctly describes the energetics of the "8" Cu oxide, which is an ultra-thin oxide film on Cu(111). The universal potential model even predicts a more favorable structure compared to that discussed in recent DFT-based literature. Moving to sulfur adatom overlayers on Cu(111), Ag(111), and Au(111) the CHGNet model, however, requires corrections. We demonstrate that these are efficiently provided via the GPR-based $Δ$-model formulated on the CHGNet's own internal atomic embedding representation. The need for corrections is tracked to the scarcity of metal-sulfur atomic environments in the materials project database that CHGNet is trained on leading to an overreliance on sulfur-sulfur atomic environments. Other universal potentials trained on the same data, MACE-MP0, SevenNet-0, and ORB-v2-only-MPtrj show similar behavior, but with varying degrees of error, demonstrating the general need for augmentation schemes for universal potential models. |
| title | $Δ$-model correction of Foundation Model based on the models own understanding |
| topic | Materials Science |
| url | https://arxiv.org/abs/2502.21179 |