Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929540896391168 |
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| author | Ren, Yuxuan Zheng, Dihan Liu, Chang Jin, Peiran Shi, Yu Huang, Lin He, Jiyan Luo, Shengjie Qin, Tao Liu, Tie-Yan |
| author_facet | Ren, Yuxuan Zheng, Dihan Liu, Chang Jin, Peiran Shi, Yu Huang, Lin He, Jiyan Luo, Shengjie Qin, Tao Liu, Tie-Yan |
| contents | In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm. Nevertheless, data of different molecular properties are often not aligned: some quantities, e.g. equilibrium structure, demand more cost to compute than others, e.g. energy, so their data are often generated by cheaper computational methods at the cost of lower accuracy, which cannot be directly overcome through multi-task learning. Moreover, it is not straightforward to leverage abundant data of other tasks to benefit a particular task. To handle such data heterogeneity challenges, we exploit the specialty of molecular tasks that there are physical laws connecting them, and design consistency training approaches that allow different tasks to exchange information directly so as to improve one another. Particularly, we demonstrate that the more accurate energy data can improve the accuracy of structure prediction. We also find that consistency training can directly leverage force and off-equilibrium structure data to improve structure prediction, demonstrating a broad capability for integrating heterogeneous data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_10118 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning Ren, Yuxuan Zheng, Dihan Liu, Chang Jin, Peiran Shi, Yu Huang, Lin He, Jiyan Luo, Shengjie Qin, Tao Liu, Tie-Yan Machine Learning Chemical Physics In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm. Nevertheless, data of different molecular properties are often not aligned: some quantities, e.g. equilibrium structure, demand more cost to compute than others, e.g. energy, so their data are often generated by cheaper computational methods at the cost of lower accuracy, which cannot be directly overcome through multi-task learning. Moreover, it is not straightforward to leverage abundant data of other tasks to benefit a particular task. To handle such data heterogeneity challenges, we exploit the specialty of molecular tasks that there are physical laws connecting them, and design consistency training approaches that allow different tasks to exchange information directly so as to improve one another. Particularly, we demonstrate that the more accurate energy data can improve the accuracy of structure prediction. We also find that consistency training can directly leverage force and off-equilibrium structure data to improve structure prediction, demonstrating a broad capability for integrating heterogeneous data. |
| title | Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning |
| topic | Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2410.10118 |