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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2605.22601 |
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| _version_ | 1866917520274882560 |
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| author | Hložný, Adam Brndiar, Ján Luo, Ye Štich, Ivan |
| author_facet | Hložný, Adam Brndiar, Ján Luo, Ye Štich, Ivan |
| contents | We designed a procedure to train a machine learning interatomic potential (MLIP) at benchmark-quality quantum Monte Carlo (QMC) accuracy. To avoid the complexities of high-quality atomic force determination with the stochastic QMC methods, we use a multi-fidelity approach wherein high-level QMC energies are used alongside suitably processed low-level DFT atomic forces to train a QMC fine-tuned MLIP which significantly improves both the energetics and atomic forces over the baseline DFT-based MLIP. Fine-tuning is only applied to the readout layers of an equivariant message-passing MACE MLIP. We used sulfur mono- and multiple vacancies in monolayer MoS$_2$ as a testbed and demonstrate a near QMC accuracy of the model in a number of in- and out-of-domain tests. We show that a fairly limited dataset of QMC energies suffice to significantly improve the baseline DFT MLIP. The accuracy of our approach is demonstrated on energy and free energy migration barriers of mono- and multiple S-vacancy defects. The results open the window to large-scale near QMC quality simulations with large numbers of atoms and/or molecular dynamics configurations which would not be possible by a direct brute-force application of QMC methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22601 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Upscaling DFT-trained machine-learning interatomic potential toward Quantum Monte Carlo accuracy: Sulfur-vacancy migration in monolayer MoS$_2$ as a testbed Hložný, Adam Brndiar, Ján Luo, Ye Štich, Ivan Materials Science We designed a procedure to train a machine learning interatomic potential (MLIP) at benchmark-quality quantum Monte Carlo (QMC) accuracy. To avoid the complexities of high-quality atomic force determination with the stochastic QMC methods, we use a multi-fidelity approach wherein high-level QMC energies are used alongside suitably processed low-level DFT atomic forces to train a QMC fine-tuned MLIP which significantly improves both the energetics and atomic forces over the baseline DFT-based MLIP. Fine-tuning is only applied to the readout layers of an equivariant message-passing MACE MLIP. We used sulfur mono- and multiple vacancies in monolayer MoS$_2$ as a testbed and demonstrate a near QMC accuracy of the model in a number of in- and out-of-domain tests. We show that a fairly limited dataset of QMC energies suffice to significantly improve the baseline DFT MLIP. The accuracy of our approach is demonstrated on energy and free energy migration barriers of mono- and multiple S-vacancy defects. The results open the window to large-scale near QMC quality simulations with large numbers of atoms and/or molecular dynamics configurations which would not be possible by a direct brute-force application of QMC methods. |
| title | Upscaling DFT-trained machine-learning interatomic potential toward Quantum Monte Carlo accuracy: Sulfur-vacancy migration in monolayer MoS$_2$ as a testbed |
| topic | Materials Science |
| url | https://arxiv.org/abs/2605.22601 |