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| Autori principali: | , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.22938 |
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| _version_ | 1866914199312007168 |
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| author | Allam, Omar Wander, Brook Kim, SungYeon Plesch, Rudi Sours, Tyler Chu, Jia-Min Ludwig, Thomas Kim, Jiyoon Wang, Rodrigo Agarwal, Shivang Rask, Alan Fleury, Alexandre Wang, Chuhong Wildman, Andrew Mustard, Thomas Ryczko, Kevin Abruzzo, Paul Nish, AJ Singh, Aayush R. |
| author_facet | Allam, Omar Wander, Brook Kim, SungYeon Plesch, Rudi Sours, Tyler Chu, Jia-Min Ludwig, Thomas Kim, Jiyoon Wang, Rodrigo Agarwal, Shivang Rask, Alan Fleury, Alexandre Wang, Chuhong Wildman, Andrew Mustard, Thomas Ryczko, Kevin Abruzzo, Paul Nish, AJ Singh, Aayush R. |
| contents | Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data. To extend these capabilities, we introduce AQCat25, a complementary dataset of 13.5 million density functional theory (DFT) single point calculations designed to improve the treatment of systems where spin polarization and/or higher fidelity are critical. We also investigate methodologies for integrating new datasets, such as AQCat25, with the broader Open Catalyst 2020 (OC20) dataset to create spin-aware models without sacrificing generalizability. We find that directly tuning a general model on AQCat25 leads to catastrophic forgetting of the original dataset's knowledge. Conversely, joint training strategies prove effective for improving accuracy on the new data without sacrificing general performance. This joint approach introduces a challenge, as the model must learn from a dataset containing both mixed-fidelity calculations and mixed-physics (spin-polarized vs. unpolarized). We show that explicitly conditioning the model on this system-specific metadata, for example by using Feature-wise Linear Modulation (FiLM), successfully addresses this challenge and further enhances model accuracy. Ultimately, our work establishes an effective protocol for bridging DFT fidelity domains to advance the predictive power of foundational models in catalysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis Allam, Omar Wander, Brook Kim, SungYeon Plesch, Rudi Sours, Tyler Chu, Jia-Min Ludwig, Thomas Kim, Jiyoon Wang, Rodrigo Agarwal, Shivang Rask, Alan Fleury, Alexandre Wang, Chuhong Wildman, Andrew Mustard, Thomas Ryczko, Kevin Abruzzo, Paul Nish, AJ Singh, Aayush R. Materials Science Machine Learning Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data. To extend these capabilities, we introduce AQCat25, a complementary dataset of 13.5 million density functional theory (DFT) single point calculations designed to improve the treatment of systems where spin polarization and/or higher fidelity are critical. We also investigate methodologies for integrating new datasets, such as AQCat25, with the broader Open Catalyst 2020 (OC20) dataset to create spin-aware models without sacrificing generalizability. We find that directly tuning a general model on AQCat25 leads to catastrophic forgetting of the original dataset's knowledge. Conversely, joint training strategies prove effective for improving accuracy on the new data without sacrificing general performance. This joint approach introduces a challenge, as the model must learn from a dataset containing both mixed-fidelity calculations and mixed-physics (spin-polarized vs. unpolarized). We show that explicitly conditioning the model on this system-specific metadata, for example by using Feature-wise Linear Modulation (FiLM), successfully addresses this challenge and further enhances model accuracy. Ultimately, our work establishes an effective protocol for bridging DFT fidelity domains to advance the predictive power of foundational models in catalysis. |
| title | AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2510.22938 |