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Many-Body Permutationally Invariant Polynomial Neural Network Potential Energy Surface for N4.
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2020-07-01 , DOI: 10.1021/acs.jctc.0c00430
Jun Li 1, 2 , Zoltan Varga 3 , Donald G Truhlar 3 , Hua Guo 1
Affiliation  

A potential energy surface (PES) for high-energy collisions between nitrogen molecules is useful for modeling chemical dynamics in shock waves and plasmas. In the present work, we fit the many-body (MB) component of the ground-state PES of N4 to an analytic function using neural networks (NNs) with permutationally invariant polynomials (PIPs). The fitting dataset of the N4 system is an extension of one used previously, extended with 4859 new CASPT2 points and 13 new CCSD(T) points to reach a total of 21 406 points. The MB-PIP-NN fit required a very complete coverage of the geometry domain in order to get a physical fit, and we devised several tactical steps to achieve this, including trajectory calculations, the comparison of the NN fit with PIP fits with mixed-exponential Gaussian bond order variables, and searching geometry regions with sparse data coverage. With these efforts, the final dataset is more suitable for a NN fit. The energy range of the dataset is much wider than those used in other systems previously fitted by NNs, and there are more rugged surface regions than usual due to locally avoided crossings. The performance of the new MB-PIP-NN fit is compared to that of another new fit to the same data by least-squares methods not employing NNs, and the mean unsigned deviation from the electronic structure calculations is reduced by a factor of 3, although the computing time to calculate a force for a trajectory calculation is larger by an order of magnitude. The accuracy achievable with the NN fit for this difficult system is very impressive, and we anticipate that the NN method can give useful fits to difficult cases that cannot be achieved by more conventional methods.

中文翻译:

N4的多体置换不变多项式神经网络势能面。

氮分子之间发生高能碰撞的势能面(PES)可用于模拟冲击波和等离子体中的化学动力学。在当前的工作中,我们使用带有置换不变多项式(PIP)的神经网络(NN)将N 4的基态PES的多体(MB)分量拟合为解析函数。N 4的拟合数据集系统是先前使用的系统的扩展,扩展了4859个新的CASPT2点和13个新的CCSD(T)点,总计达到21 406点。MB-PIP-NN拟合需要对几何域进行非常完整的覆盖才能获得物理拟合,我们设计了一些战术步骤来实现这一目标,包括轨迹计算,NN拟合与PIP拟合与混合拟合的比较。指数高斯键序变量,并使用稀疏数据覆盖范围搜索几何区域。通过这些努力,最终数据集更适合于NN拟合。数据集的能量范围比以前由NN拟合的其他系统中使用的能量范围要宽得多,并且由于局部避免了交叉,因此比通常情况下表面区域更崎rug。通过不使用NN的最小二乘法,将新的MB-PIP-NN拟合的性能与另一个新的拟合结果进行了比较,并且与电子结构的平均无符号偏差降低了3倍,尽管计算用于轨迹计算的力的计算时间要大一个数量级。NN拟合对于这个困难的系统可以实现的精度非常令人印象深刻,并且我们期望NN方法可以为较困难的情况提供有用的拟合,而这些情况是用常规方法无法实现的。
更新日期:2020-08-11
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