Molecular bond energy is a key parameter for analyzing the properties of chemical activity, stability and flexibility. Calculating bond energy is a challenge due to the cost of first-principles simulations and unsatisfactory prediction using empirical formula. Here we show that a neural network (NN) machine-learning method can achieve quick prediction of bond energies of organic molecules. Using atomic species and charge information as descriptors, we trained a NN protocol and applied it to predict the bond energy in a certain chemical bond that agreed with density functional theory calculations. This protocol also provided a way to evaluate the effects of different methods of atomic charge analysis on NN training. Trained to accurately estimate bond energies, this NN protocol provides a cost-effective tool for optimizing chemical reactions, accelerating molecular design, and other important applications.

中文翻译：

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用于预测分子键能的神经网络协议

分子键能是分析化学活性，稳定性和柔韧性的关键参数。由于第一性原理模拟的成本以及使用经验公式进行的预测不理想，因此计算键能是一项挑战。在这里，我们证明了一种神经网络（NN）机器学习方法可以实现对有机分子键能的快速预测。使用原子种类和电荷信息作为描述符，我们训练了一种NN协议，并将其应用于预测与密度泛函理论计算相符的某个化学键中的键能。该协议还提供了一种方法，用于评估不同原子电荷分析方法对NN训练的影响。经过训练可精确估算键能，该NN协议为优化化学反应提供了一种经济高效的工具，