Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
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2025
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| author | Benali, Anouar Plé, Thomas Adjoua, Olivier Agarawal, Valay Applencourt, Thomas Blazhynska, Marharyta Clay III, Raymond Gasperich, Kevin Hossain, Khalid Kim, Jeongnim Knight, Christopher Krogel, Jaron T. Maday, Yvon Maria, Maxime Montes, Matthieu Luo, Ye Posenitskiy, Evgeny Villot, Corentin Vishwanath, Venkatram Lagardère, Louis Piquemal, Jean-Philip |
| author_facet | Benali, Anouar Plé, Thomas Adjoua, Olivier Agarawal, Valay Applencourt, Thomas Blazhynska, Marharyta Clay III, Raymond Gasperich, Kevin Hossain, Khalid Kim, Jeongnim Knight, Christopher Krogel, Jaron T. Maday, Yvon Maria, Maxime Montes, Matthieu Luo, Ye Posenitskiy, Evgeny Villot, Corentin Vishwanath, Venkatram Lagardère, Louis Piquemal, Jean-Philip |
| contents | We propose an end-to-end integrated strategy to produce highly accurate quantum chemistry (QC) synthetic datasets (energies and forces) aimed at deriving Foundation Machine Learning models for molecular simulation. Starting from Density Functional Theory (DFT), a "Jacob's Ladder" approach leverages computationally-optimized layers of massively GPU-accelerated software with increasing accuracy. Thanks to Exascale, this is the first time that the computationally intensive calculation of Quantum Monte Carlo forces (QMC), and the combination of multi-determinant QMC energies and forces with selected-Configuration Interaction wavefunctions, are computed at such scale at the complete basis-set limit. To bridge the gap between accurate QC and condensed-phase Molecular Dynamics, we leverage transfer learning to improve the DFT-based FeNNix-Bio1 foundation model. The resulting approach is coupled to path integrals adaptive sampling quantum dynamics to perform nanosecond reactive simulations at unprecedented accuracy. These results demonstrate the promise of Exascale to deepen our understanding of the inner machinery of complex biosystems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07948 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals Benali, Anouar Plé, Thomas Adjoua, Olivier Agarawal, Valay Applencourt, Thomas Blazhynska, Marharyta Clay III, Raymond Gasperich, Kevin Hossain, Khalid Kim, Jeongnim Knight, Christopher Krogel, Jaron T. Maday, Yvon Maria, Maxime Montes, Matthieu Luo, Ye Posenitskiy, Evgeny Villot, Corentin Vishwanath, Venkatram Lagardère, Louis Piquemal, Jean-Philip Chemical Physics We propose an end-to-end integrated strategy to produce highly accurate quantum chemistry (QC) synthetic datasets (energies and forces) aimed at deriving Foundation Machine Learning models for molecular simulation. Starting from Density Functional Theory (DFT), a "Jacob's Ladder" approach leverages computationally-optimized layers of massively GPU-accelerated software with increasing accuracy. Thanks to Exascale, this is the first time that the computationally intensive calculation of Quantum Monte Carlo forces (QMC), and the combination of multi-determinant QMC energies and forces with selected-Configuration Interaction wavefunctions, are computed at such scale at the complete basis-set limit. To bridge the gap between accurate QC and condensed-phase Molecular Dynamics, we leverage transfer learning to improve the DFT-based FeNNix-Bio1 foundation model. The resulting approach is coupled to path integrals adaptive sampling quantum dynamics to perform nanosecond reactive simulations at unprecedented accuracy. These results demonstrate the promise of Exascale to deepen our understanding of the inner machinery of complex biosystems. |
| title | Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2504.07948 |