Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States.
J Chem Inf Model. 2021 Jun 28;61(6):2798-2805. doi: 10.1021/acs.jcim.1c00367. Epub 2021 May 25.
Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemistry methods, approximate methods based on additivity schemes and more recently machine learning are currently the only approaches capable of supplying the chemical coverage and throughput necessary for such applications. For both approaches, ring-containing molecules pose a challenge to transferability due to the nonlocal interactions associated with conjugation and strain that significantly impact thermodynamic properties. Here, we report the development of a self-consistent approach for parameterizing transferable ring corrections based on high-level quantum chemistry. The method is benchmarked against both the Pedley-Naylor-Kline experimental dataset for C-, H-, O-, N-, S-, and halogen-containing cyclic molecules and a dataset of Gaussian-4 quantum chemistry calculations. The prescribed approach is demonstrated to be superior to existing ring corrections while maintaining extensibility to arbitrary chemistries. We have also compared this ring-correction scheme against a novel machine learning approach and demonstrate that the latter is capable of exceeding the performance of physics-based ring corrections.
计算预测分子和材料的热力学性质在当代反应预测和动力学建模中起着核心作用。由于缺乏实验数据和高水平量子化学方法的计算成本，基于加和方案的近似方法和最近的机器学习方法是目前唯一能够提供此类应用所需的化学覆盖和通量的方法。对于这两种方法，由于共轭和应变引起的非局部相互作用会显著影响热力学性质，因此含有环的分子在可转移性方面存在挑战。在这里，我们报告了一种基于高精度量子化学的参数化可转移环校正的自洽方法的开发。该方法与包含 C、H、O、N、S 和卤素的环状分子的 Pedley-Naylor-Kline 实验数据集以及高斯-4 量子化学计算数据集进行了基准测试。所规定的方法被证明优于现有的环校正方法，同时保持对任意化学物质的可扩展性。我们还将这种环校正方案与一种新的机器学习方法进行了比较，并证明后者能够超越基于物理的环校正方法的性能。
