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:尽管近年来在加速量子化学计算的新算法和计算方法方面出现了大幅增长,但基于轨迹的方法研究光学诱发过程的瓶颈仍然是大量电子结构计算。在这项工作中,我们提出了一个创新解决方案,即采用机器学习算法和从人工智能领域借用的方法,大幅降低电子结构计算的数量。然而,有效应用这些算法需要找到最佳超参数,而这本身仍然是一个挑战。在这里,我们提出了一个自动的用户友好框架HAAX,以对神经网络进行超光度优化,这绕过了漫长人工过程的需要。神经网络生成了潜在的能源表面(PES),降低了计算成本,与基于初始的PES相比,我们通过使用机器学习算法和从人工智能领域借用的方法,对不同基于超光度的选比值算算法的性能进行了比较调查,即电网搜索、模拟内分光度计、遗传算法和对在寻找最佳超光度的神经网络进行超光度优化,以找到最佳的超光度优化的内压网络,而不是在建立最佳的自动超光速的轨道上,从而显示我们所需的最佳的机化的轨道,从而实现最佳的系统的精确的轨道,从而实现最佳的轨道,从而实现最佳的系统。