Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916071105101824 |
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| author | Zheng, Tianze Xu, Xingyuan Wang, Zhi Yang, Zhenze Wang, Yuanheng Han, Xu Chen, Lei Mu, Zhenliang Zhang, Ziqing Liu, Siyuan Gong, Sheng Yu, Kuang Yan, Wen |
| author_facet | Zheng, Tianze Xu, Xingyuan Wang, Zhi Yang, Zhenze Wang, Yuanheng Han, Xu Chen, Lei Mu, Zhenliang Zhang, Ziqing Liu, Siyuan Gong, Sheng Yu, Kuang Yan, Wen |
| contents | Molecular dynamics (MD) simulations are essential tools for unraveling atomistic insights into the structure and dynamics of condensed-phase systems. However, the universal and accurate prediction of macroscopic properties from ab initio calculations remains a significant challenge, often hindered by the trade-off between computational cost and simulation accuracy. Here, we present ByteFF-Pol, a graph neural network (GNN)-parameterized polarizable force field, trained exclusively on high-level quantum mechanics (QM) data. Leveraging physically-motivated force field forms and training strategies, ByteFF-Pol exhibits exceptional performance in predicting thermodynamic and transport properties for a wide range of small-molecule liquids and electrolytes, outperforming state-of-the-art (SOTA) classical and machine learning force fields. The zero-shot prediction capability of ByteFF-Pol bridges the gap between microscopic QM calculations and macroscopic liquid properties, enabling the exploration of previously intractable chemical spaces. This advancement holds transformative potential for applications such as electrolyte design and custom-tailored solvent, representing a pivotal step toward data-driven materials discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08575 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field Zheng, Tianze Xu, Xingyuan Wang, Zhi Yang, Zhenze Wang, Yuanheng Han, Xu Chen, Lei Mu, Zhenliang Zhang, Ziqing Liu, Siyuan Gong, Sheng Yu, Kuang Yan, Wen Computational Physics Chemical Physics Molecular dynamics (MD) simulations are essential tools for unraveling atomistic insights into the structure and dynamics of condensed-phase systems. However, the universal and accurate prediction of macroscopic properties from ab initio calculations remains a significant challenge, often hindered by the trade-off between computational cost and simulation accuracy. Here, we present ByteFF-Pol, a graph neural network (GNN)-parameterized polarizable force field, trained exclusively on high-level quantum mechanics (QM) data. Leveraging physically-motivated force field forms and training strategies, ByteFF-Pol exhibits exceptional performance in predicting thermodynamic and transport properties for a wide range of small-molecule liquids and electrolytes, outperforming state-of-the-art (SOTA) classical and machine learning force fields. The zero-shot prediction capability of ByteFF-Pol bridges the gap between microscopic QM calculations and macroscopic liquid properties, enabling the exploration of previously intractable chemical spaces. This advancement holds transformative potential for applications such as electrolyte design and custom-tailored solvent, representing a pivotal step toward data-driven materials discovery. |
| title | Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field |
| topic | Computational Physics Chemical Physics |
| url | https://arxiv.org/abs/2508.08575 |