State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
Fudan University, Shanghai, 200032, China.
Phys Chem Chem Phys. 2023 Mar 15;25(11):8117-8127. doi: 10.1039/d3cp00312d.
We report here a full-dimensional machine learning global potential surface (PES) for the rearrangement of methylhydroxycarbene (HC-C-OH, 1t). The PES is trained with the fundamental invariant neural network (FI-NN) method on 91 564 energies calculated at the UCCSD(T)-F12a/cc-pVTZ level of theory, covering three possible product channels. FI-NN PES has the correct symmetry properties with respect to permutation of four identical hydrogen atoms and is suitable for dynamics studies of the 1t rearrangement. The averaged root mean square error (RMSE) is 11.4 meV. Six important reaction pathways, as well as the energies and vibrational frequencies at the stationary geometries on these pathways are accurately preproduced by our FI-NN PES. To demonstrate the capacity of the PES, we calculated the rate coefficient of hydrogen migration in -CH (path A) and hydrogen migration of -OH (path B) with instanton theory on this PES. Our calculations predicted the half-life of 1t to be 95 min, which is excellent in agreement with experimental observations.
我们在这里报告了一个完整的机器学习全局势能面（PES），用于甲基羟甲叉（HC-C-OH，1t）的重排。该 PES 是使用基本不变神经网络（FI-NN）方法在 UCCSD(T)-F12a/cc-pVTZ 理论水平上计算的 91,564 个能量上进行训练的，涵盖了三个可能的产物通道。FI-NN PES 具有相对于四个相同氢原子的置换的正确对称性质，适用于 1t 重排的动力学研究。平均均方根误差（RMSE）为 11.4 meV。六个重要的反应途径，以及这些途径上的稳定几何结构的能量和振动频率，都被我们的 FI-NN PES 准确地预生成。为了展示 PES 的能力，我们在这个 PES 上使用瞬时理论计算了-CH（路径 A）中的氢迁移和-OH（路径 B）中的氢迁移的速率常数。我们的计算预测 1t 的半衰期为 95 分钟，与实验观察结果非常吻合。
