A composite method, named ANN-G3S, is introduced, adapting from G3S theory and employing distinct sets of multiplicative scale factors. An artificial neural network (ANN)-based classification model is utilized to select optimal sets of four scale factors for electronic correlation and basis set expansion terms in electronic systems. The correlation and basis set terms are scaled by four parameters, two for atoms and the other two for molecules. The ANN model is trained on the G3/05 test set to identify the best parameter set for each electronic system. To validate the method, 10% of the structures from the test set are randomly excluded from training and optimization, forming a separate validation set. The method demonstrates a mean deviation of 1.11 kcal mol-1 for the G3/05 set and 0.89 kcal mol-1 for the validation set, close to the value presented by the G4 method and surpassing the accuracy of the G3 method of 1.19 kcal mol-1 with significantly reduced computational cost. This method shows advantages by eliminating the need for purely empirical corrections, thereby enhancing both efficiency and accuracy in predicting heats of formation.

中文翻译：

---

一种基于神经网络的初步复合方法，用于准确预测形成焓。


引入了一种名为 ANN-G3S 的复合方法，该方法改编自 G3S 理论并采用不同的乘法比例因子集。基于人工神经网络 （ANN） 的分类模型用于为电子系统中的电子相关和基集扩展项选择四个比例因子的最佳集合。相关性和基集项按四个参数缩放，两个用于原子，另外两个用于分子。ANN 模型在 G3/05 测试集上进行训练，以确定每个电子系统的最佳参数集。为了验证该方法，测试集中 10% 的结构被随机排除在训练和优化之外，形成一个单独的验证集。该方法表明，G3/05 集的平均偏差为 1.11 kcal mol-1，验证集的平均偏差为 0.89 kcal mol-1，接近 G4 方法提供的值，超过了 G3 方法的准确度 1.19 kcal mol-1，计算成本显著降低。这种方法通过消除纯粹的经验校正的需要而显示出优势，从而提高了预测形成热的效率和准确性。