Data Platform Center, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.
J Chem Phys. 2019 Apr 7;150(13):134103. doi: 10.1063/1.5078394.
We propose a data sampling scheme for high-dimensional neural network potentials that can predict energies along the reaction pathway calculated using the hybrid density functional theory. We observed that a data sampling scheme that combined partial geometry optimization of intermediate structures with random displacement of atoms successfully predicted the energies along the reaction path with respect to five chemical reactions: Claisen rearrangement, Diels-Alder reaction, [1,5]-sigmatropic hydrogen shift, concerted hydrogen transfer in the water hexamer, and Cornforth rearrangement.
我们提出了一种针对高维神经网络势的数据分析方案，该方案可以预测使用混合密度泛函理论计算的反应途径上的能量。我们发现，一种将中间结构的部分几何优化与原子的随机位移相结合的数据采样方案成功地预测了五个化学反应：克莱森重排、Diels-Alder 反应、[1,5]-西格玛迁移、水六聚体中的协同氢转移和康福思重排。
