Bonding characters, such as length and strength, are of key importance for material structure and properties. Here, a machine learning (ML) model is used to predict the bonding properties from information pertaining to isolated systems before bonding. This model employs the density of states (DOS) before bond formation as the ML descriptor and accurately predicts the binding energy, bond distance, covalent electron amount, and Fermi energy even when only 20% of the whole dataset is used for training. The results show that the DOS of isolated systems before bonding is a powerful descriptor for the prediction of bonding and adsorption properties.

中文翻译：

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通过基于机器学习的模型在键合前使用孤立状态准确预测键合特性

粘合特性，例如长度和强度，对于材料结构和性能至关重要。在这里，机器学习 (ML) 模型用于在粘合之前根据与孤立系统相关的信息来预测粘合特性。该模型使用键形成前的状态密度 (DOS) 作为 ML 描述符，即使仅使用整个数据集的 20% 进行训练，也能准确预测结合能、键距离、共价电子量和费米能。结果表明，键合前孤立系统的 DOS 是预测键合和吸附性质的有力描述符。