Accurate Thermophysical Properties of Water using Machine-Learned Potentials
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866908795855175680 |
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| author | Hilpert, Tobias Kresse, Georg |
| author_facet | Hilpert, Tobias Kresse, Georg |
| contents | Simulating water from first principles remains a significant computational challenge due to the slow dynamics of the underlying system. Although machine-learned interatomic potentials (MLPs) can accelerate these simulations, they often fail to achieve the required level of accuracy for reliable uncertainty quantification. In this study, we use MACE - an equivariant graph neural network architecture that has been trained using an extensive RPBE-D3 database - to predict density isobars, diffusion constants, radial distribution functions, and melting points. Although equivariant MACE models are computationally more expensive than simpler architectures, such as kernel-based potentials (KbPs), their significantly lower total energy errors allow for reliable thermodynamic reweighting with minimal bias. Our results are consistent with those of previous studies using KbPs; however, equivariant models can be validated against the ground-truth density functional theory (DFT) ensemble, providing a critical advantage. These findings establish equivariant MLPs as robust and reliable tools for investigating the thermophysical properties of water with DFT-level accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21103 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accurate Thermophysical Properties of Water using Machine-Learned Potentials Hilpert, Tobias Kresse, Georg Chemical Physics Soft Condensed Matter Simulating water from first principles remains a significant computational challenge due to the slow dynamics of the underlying system. Although machine-learned interatomic potentials (MLPs) can accelerate these simulations, they often fail to achieve the required level of accuracy for reliable uncertainty quantification. In this study, we use MACE - an equivariant graph neural network architecture that has been trained using an extensive RPBE-D3 database - to predict density isobars, diffusion constants, radial distribution functions, and melting points. Although equivariant MACE models are computationally more expensive than simpler architectures, such as kernel-based potentials (KbPs), their significantly lower total energy errors allow for reliable thermodynamic reweighting with minimal bias. Our results are consistent with those of previous studies using KbPs; however, equivariant models can be validated against the ground-truth density functional theory (DFT) ensemble, providing a critical advantage. These findings establish equivariant MLPs as robust and reliable tools for investigating the thermophysical properties of water with DFT-level accuracy. |
| title | Accurate Thermophysical Properties of Water using Machine-Learned Potentials |
| topic | Chemical Physics Soft Condensed Matter |
| url | https://arxiv.org/abs/2601.21103 |