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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.05652 |
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| _version_ | 1866912367222194176 |
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| author | Novelli, Pietro Meanti, Giacomo Buigues, Pedro J. Rosasco, Lorenzo Parrinello, Michele Pontil, Massimiliano Bonati, Luigi |
| author_facet | Novelli, Pietro Meanti, Giacomo Buigues, Pedro J. Rosasco, Lorenzo Parrinello, Michele Pontil, Massimiliano Bonati, Luigi |
| contents | Training machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To this effect, we introduce franken, a scalable and lightweight transfer learning framework that extracts atomic descriptors from pretrained graph neural networks and transfer them to new systems using random Fourier features-an efficient and scalable approximation of kernel methods. Franken enables fast and accurate adaptation of general-purpose potentials to new systems or levels of quantum mechanical theory without requiring hyperparameter tuning or architectural modifications. On a benchmark dataset of 27 transition metals, franken outperforms optimized kernel-based methods in both training time and accuracy, reducing model training from tens of hours to minutes on a single GPU. We further demonstrate the framework's strong data-efficiency by training stable and accurate potentials for bulk water and the Pt(111)/water interface using just tens of training structures. Our open-source implementation (https://franken.readthedocs.io) offers a fast and practical solution for training potentials and deploying them for molecular dynamics simulations across diverse systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05652 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast and Fourier Features for Transfer Learning of Interatomic Potentials Novelli, Pietro Meanti, Giacomo Buigues, Pedro J. Rosasco, Lorenzo Parrinello, Michele Pontil, Massimiliano Bonati, Luigi Computational Physics Training machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To this effect, we introduce franken, a scalable and lightweight transfer learning framework that extracts atomic descriptors from pretrained graph neural networks and transfer them to new systems using random Fourier features-an efficient and scalable approximation of kernel methods. Franken enables fast and accurate adaptation of general-purpose potentials to new systems or levels of quantum mechanical theory without requiring hyperparameter tuning or architectural modifications. On a benchmark dataset of 27 transition metals, franken outperforms optimized kernel-based methods in both training time and accuracy, reducing model training from tens of hours to minutes on a single GPU. We further demonstrate the framework's strong data-efficiency by training stable and accurate potentials for bulk water and the Pt(111)/water interface using just tens of training structures. Our open-source implementation (https://franken.readthedocs.io) offers a fast and practical solution for training potentials and deploying them for molecular dynamics simulations across diverse systems. |
| title | Fast and Fourier Features for Transfer Learning of Interatomic Potentials |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2505.05652 |