基于机器学习的键合前孤立态模型精确预测键合性能
Accurate Prediction of Bonding Properties by A Machine Learning-based Model using Isolated States Before Bonding
摘要：
鉴于材料结构和性能对组分键的长度和强度的强烈依赖性，以及表面吸附和化学反应是由两个系统之间形成键引发的事实，键合参数对于材料设计和工业过程至关重要。在这项研究中，一个机器学习（ML）为基础的模型被用来准确地预测键合性能的信息有关的孤立系键合前。该模型采用键形成前的态密度（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、共价电子量和费米能。结果表明，成键前孤立系的DOS是准确预测成键和吸附性质的有力描述符。
英文摘要：
Given the strong dependence of material structure and properties on the length and strength of constituent bonds and the fact that surface adsorption and chemical reactions are initiated by the formation of bonds between two systems, bonding parameters are of key importance for material design and industrial processes. In this study, a machine learning (ML)-based model is used to accurately predict 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 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 accurate prediction of bonding and adsorption properties.
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。
