Towards a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909299283853312 |
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| author | Xu, Fanjie Guo, Wentao Wang, Feng Yao, Lin Wang, Hongshuai Tang, Fujie Gao, Zhifeng Zhang, Linfeng E, Weinan Tian, Zhong-Qun Cheng, Jun |
| author_facet | Xu, Fanjie Guo, Wentao Wang, Feng Yao, Lin Wang, Hongshuai Tang, Fujie Gao, Zhifeng Zhang, Linfeng E, Weinan Tian, Zhong-Qun Cheng, Jun |
| contents | The study of structure-spectrum relationships is essential for spectral interpretation, impacting structural elucidation and material design. Predicting spectra from molecular structures is challenging due to their complex relationships. Herein, we introduce NMRNet, a deep learning framework using the SE(3) Transformer for atomic environment modeling, following a pre-training and fine-tuning paradigm. To support the evaluation of NMR chemical shift prediction models, we have established a comprehensive benchmark based on previous research and databases, covering diverse chemical systems. Applying NMRNet to these benchmark datasets, we achieve state-of-the-art performance in both liquid-state and solid-state NMR datasets, demonstrating its robustness and practical utility in real-world scenarios. This marks the first integration of solid and liquid state NMR within a unified model architecture, highlighting the need for domainspecific handling of different atomic environments. Our work sets a new standard for NMR prediction, advancing deep learning applications in analytical and structural chemistry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15681 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts Xu, Fanjie Guo, Wentao Wang, Feng Yao, Lin Wang, Hongshuai Tang, Fujie Gao, Zhifeng Zhang, Linfeng E, Weinan Tian, Zhong-Qun Cheng, Jun Computational Physics Disordered Systems and Neural Networks Materials Science Chemical Physics The study of structure-spectrum relationships is essential for spectral interpretation, impacting structural elucidation and material design. Predicting spectra from molecular structures is challenging due to their complex relationships. Herein, we introduce NMRNet, a deep learning framework using the SE(3) Transformer for atomic environment modeling, following a pre-training and fine-tuning paradigm. To support the evaluation of NMR chemical shift prediction models, we have established a comprehensive benchmark based on previous research and databases, covering diverse chemical systems. Applying NMRNet to these benchmark datasets, we achieve state-of-the-art performance in both liquid-state and solid-state NMR datasets, demonstrating its robustness and practical utility in real-world scenarios. This marks the first integration of solid and liquid state NMR within a unified model architecture, highlighting the need for domainspecific handling of different atomic environments. Our work sets a new standard for NMR prediction, advancing deep learning applications in analytical and structural chemistry. |
| title | Towards a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts |
| topic | Computational Physics Disordered Systems and Neural Networks Materials Science Chemical Physics |
| url | https://arxiv.org/abs/2408.15681 |