Computational chemistry has become an important tool to predict and understand molecular properties and reactions. Even though recent years have seen a significant growth in new algorithms and computational methods that speed up quantum chemical calculations, the bottleneck for trajectory-based methods to study photoinduced processes is still the huge number of electronic structure calculations. In this work, we present an innovative solution, in which the amount of electronic structure calculations is drastically reduced, by employing machine learning algorithms and methods borrowed from the realm of artificial intelligence. However, applying these algorithms effectively requires finding optimal hyperparameters, which remains a challenge itself. Here we present an automated user-friendly framework, HOAX, to perform the hyperparameter optimization for neural networks, which bypasses the need for a lengthy manual process. The neural network generated potential energy surfaces (PESs) reduces the computational costs compared to the ab initio-based PESs. We perform a comparative investigation on the performance of different hyperparameter optimiziation algorithms, namely grid search, simulated annealing, genetic algorithm, and bayesian optimizer in finding the optimal hyperparameters necessary for constructing the well-performing neural network in order to fit the PESs of small organic molecules. Our results show that this automated toolkit not only facilitate a straightforward way to perform the hyperparameter optimization but also the resulting neural networks-based generated PESs are in reasonable agreement with the ab initio-based PESs.

翻译：计算化学已成为预测和理解分子性质及反应的重要工具。尽管近年来新算法和计算方法的显著增长加速了量子化学计算，但基于轨迹方法研究光诱导过程的瓶颈仍然是电子结构计算的庞大数量。本文提出了一种创新解决方案，通过采用机器学习算法和人工智能领域的方法，大幅减少了电子结构计算量。然而，有效应用这些算法需要寻找最优超参数，这本身仍是一项挑战。我们在此呈现了一个自动化用户友好框架HOAX，用于执行神经网络的超参数优化，从而省去耗时的手动调整过程。与基于从头算的势能面相比，神经网络生成的势能面显著降低了计算成本。我们比较研究了不同超参数优化算法（即网格搜索、模拟退火、遗传算法和贝叶斯优化器）在寻找构建高性能神经网络所需最优超参数时的性能，以拟合小有机分子的势能面。结果表明，该自动化工具包不仅提供了直接的超参数优化途径，而且生成的神经网络势能面与基于从头算的势能面具有合理一致性。