Li Yanbo, Ma Fenfen, Wang Zhandong, Chen Xin
School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China.
GuSu Laboratory of Materials, Suzhou 215123, China.
J Phys Chem Lett. 2024 Nov 14;15(45):11282-11290. doi: 10.1021/acs.jpclett.4c02647. Epub 2024 Nov 4.
Accurate and efficient determination of site-specific reaction rate constants over a wide temperature range remains challenging, both experimentally and theoretically. Taking the dehydrogenation reaction as an example, our study addresses this issue by an innovative combination of machine learning techniques and cost-effective NMR spectra. Through descriptor screening, we identified a minimal set of NMR chemical shifts that can effectively determine reaction rate constants. The constructed model performs exceptionally well on theoretical data sets and demonstrates impressive generalization capabilities, extending from small molecules to larger ones. Furthermore, this model shows outstanding performance when applied to limited experimental data sets, highlighting its robust applicability and transferability. Utilizing the Sure Independence Screening and Sparsifying Operator (SISSO) algorithm, we also present an interpretable rate constant-temperature-NMR (k-T-NMR) relationship with a mathematical formula. This study reveals the great potential of combining machine learning with easily accessible spectroscopic descriptors in the study of reaction kinetics, enabling the rapid determination of reaction rate constants and promoting our understanding of reactivity.
在很宽的温度范围内准确而高效地测定位点特异性反应速率常数，无论在实验上还是理论上都仍然具有挑战性。以脱氢反应为例，我们的研究通过将机器学习技术与经济高效的核磁共振（NMR）光谱进行创新结合来解决这个问题。通过描述符筛选，我们确定了一组最少的NMR化学位移，它们能够有效地确定反应速率常数。构建的模型在理论数据集上表现出色，并展示出令人印象深刻的泛化能力，从小分子扩展到更大的分子。此外，该模型在应用于有限的实验数据集时也表现出色，突出了其强大的适用性和可转移性。利用Sure Independence Screening and Sparsifying Operator（SISSO）算法，我们还给出了一个具有数学公式的可解释的速率常数-温度-NMR（k-T-NMR）关系。这项研究揭示了在反应动力学研究中将机器学习与易于获取的光谱描述符相结合的巨大潜力，能够快速测定反应速率常数并增进我们对反应活性的理解。
