基本不变量神经网络解析梯度方法的研究

提升势能面的运行速度对于动力学模拟至关重要. 相对于计算简单、 但耗时更长的数值梯度计算, 直接求解势能面梯度的解析公式能够大幅提高势能面的运行效率. 本文发展了基本不变量神经网络解析梯度的生成方法. 计算解析梯度的代码可以通过程序自动生成. 对大量数据点进行测试后, 证明了该方法可以得到正确的势能面梯度输出结果. 通过测试不同势能面的调用时间, 发现采用解析梯度方法能够带来10倍以上的性能提升. 随着体系的增大, 这种性能提升也会越明显.

Analytical Gradient Method for Fundamental Invariant Neural Networks

It is very important for dynamic simulation to improve the running speed of the potential energy surface. Compared with the numerical gradient calculation， which is simple but time-consuming， the direct analytical formula to solve the gradient of the potential energy surface can greatly improve the running efficiency of the potential energy surface. In this work， a method for generating analytic gradients for fundamental invariant neural networks was developed. The code to calculate the analytic gradient can be generated automatically by program. After testing a large number of data points， it was found that this method can get the correct output of the gradient of potential energy surface. By measuring the calculation time of different potential energy surfaces， it was found that the analytical gradient method can bring more than a ten-fold improvement in performance. The larger the system， the more significant the performance improvement will be.