分子碰撞激发速率系数的人工神经网络估计
Rate Coefficients for the Collisional Excitation of Molecules: Estimates from an Artificial Neural Network
摘要：
人工神经网络（ANN）的研究作为一种工具，用于估计碰撞激发的分子的速率系数。这种工具的性能可以通过在碰撞引起的转换的数据集上测试来评估，对于这些数据集，速率系数是已知的：网络在该数据集的子集上进行训练，并在其余数据集上进行测试。通过这种方法获得的结果通常是准确的，在一个因素内~ 2.1（中位数值）的过渡与低激发率和~ 1.7的那些与中等或高激发率，虽然4%的人工神经网络的输出是相差10倍以上。结果表明，人工神经网络将是有价值的外推碰撞率系数的数据集，包括高的位置尚未计算的过渡。对于本文中考虑的非对称顶部分子，有利的架构是级联相关网络，其在训练过程中创建16个隐藏神经元，其中3个输入神经元用于表征过渡的性质，一个输出神经元用于提供速率系数的对数。
英文摘要：
An artificial neural network (ANN) is investigated as a tool for estimating rate coefficients for the collisional excitation of molecules. The performance of such a tool can be evaluated by testing it on a dataset of collisionally-induced transitions for which rate coefficients are already known: the network is trained on a subset of that dataset and tested on the remainder. Results obtained by this method are typically accurate to within a factor ~ 2.1 (median value) for transitions with low excitation rates and ~ 1.7 for those with medium or high excitation rates, although 4% of the ANN outputs are discrepant by a factor of 10 more. The results suggest that ANNs will be valuable in extrapolating a dataset of collisional rate coefficients to include high-lying transitions that have not yet been calculated. For the asymmetric top molecules considered in this paper, the favored architecture is a cascade-correlation network that creates 16 hidden neurons during the course of training, with 3 input neurons to characterize the nature of the transition and one output neuron to provide the logarithm of the rate coefficient.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
