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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.23064 |
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| _version_ | 1866918187904270336 |
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| author | Li, Wenwen Charoenphakdee, Nontawat Zhuang, Yong-Bin Okuno, Ryuhei Tsuboi, Yuta Takamoto, So Ishida, Junichi Li, Ju |
| author_facet | Li, Wenwen Charoenphakdee, Nontawat Zhuang, Yong-Bin Okuno, Ryuhei Tsuboi, Yuta Takamoto, So Ishida, Junichi Li, Ju |
| contents | Atomistic simulation methods have evolved through successive computational levels, each building upon more fundamental approaches: from quantum mechanics to density functional theory (DFT), and subsequently, to machine learning interatomic potentials (MLIPs). While universal MLIPs (u-MLIPs) offer broad transferability, their computational overhead limits large-scale applications. Task-specific MLIPs (ts-MLIPs) achieve superior efficiency but require prohibitively expensive DFT data generation for each material system. In this paper, we propose LightPFP, a data-efficient knowledge distillation framework. Instead of using costly DFT calculations, LightPFP generates a distilled ts-MLIP by leveraging u-MLIP to generate high-quality training data tailored for specific materials and utilizing a pre-trained light-weight MLIP to further enhance data efficiency. Across a broad spectrum of materials, including solid-state electrolytes, high-entropy alloys, and reactive ionic systems, LightPFP delivers three orders of magnitude faster model development than conventional DFT-based methods, while maintaining accuracy on par with first-principles predictions. Moreover, the distilled ts-MLIPs further sustain the computational efficiency essential for large-scale molecular dynamics, achieving 1-2 orders of magnitude faster inference than u-MLIPs. The framework further enables efficient precision transfer learning, where systematic errors from the u-MLIP can be corrected using as few as 10 high-accuracy DFT data points, as demonstrated for MgO melting point prediction. This u-MLIP-driven distillation approach enables rapid development of high-fidelity, efficient MLIPs for materials science applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23064 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale Li, Wenwen Charoenphakdee, Nontawat Zhuang, Yong-Bin Okuno, Ryuhei Tsuboi, Yuta Takamoto, So Ishida, Junichi Li, Ju Materials Science Computational Physics 68T07, 68T05, 82C32 I.6; H.2.8; J.2 Atomistic simulation methods have evolved through successive computational levels, each building upon more fundamental approaches: from quantum mechanics to density functional theory (DFT), and subsequently, to machine learning interatomic potentials (MLIPs). While universal MLIPs (u-MLIPs) offer broad transferability, their computational overhead limits large-scale applications. Task-specific MLIPs (ts-MLIPs) achieve superior efficiency but require prohibitively expensive DFT data generation for each material system. In this paper, we propose LightPFP, a data-efficient knowledge distillation framework. Instead of using costly DFT calculations, LightPFP generates a distilled ts-MLIP by leveraging u-MLIP to generate high-quality training data tailored for specific materials and utilizing a pre-trained light-weight MLIP to further enhance data efficiency. Across a broad spectrum of materials, including solid-state electrolytes, high-entropy alloys, and reactive ionic systems, LightPFP delivers three orders of magnitude faster model development than conventional DFT-based methods, while maintaining accuracy on par with first-principles predictions. Moreover, the distilled ts-MLIPs further sustain the computational efficiency essential for large-scale molecular dynamics, achieving 1-2 orders of magnitude faster inference than u-MLIPs. The framework further enables efficient precision transfer learning, where systematic errors from the u-MLIP can be corrected using as few as 10 high-accuracy DFT data points, as demonstrated for MgO melting point prediction. This u-MLIP-driven distillation approach enables rapid development of high-fidelity, efficient MLIPs for materials science applications. |
| title | LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale |
| topic | Materials Science Computational Physics 68T07, 68T05, 82C32 I.6; H.2.8; J.2 |
| url | https://arxiv.org/abs/2510.23064 |