基于机器学习训练金属离子吸附能预测模型的研究

Training Model for Predicting Adsorption Energy of Metal Ions Based on Machine Learning

本研究通过密度泛函理论对氧化石墨烯和金属离子的吸附行为进行理论模拟。基于机器学习方法训练预测模型的过程中,缺失值采用推荐系统中广泛使用的奇异值分解方法处理,并用梯度提升机解释了影响吸附能的重要因素。结果发现吸附体系中存在九种特征可为吸附能提供90%的累积重要性,分别为离子半径、零点振动能量、密立根电荷、沸点、偶极矩、原子量、摩尔定容热容、自旋多重度和键长。定量评估了六种回归方法的预测精度,包括支持向量回归、岭回归、随机森林、极端随机森林、极端梯度提升和轻梯度提升机。结果表明,机器学习方法可提供足够的吸附能预测准确性,其中极端随机森林方法表现出最优的预测性能,均方误差仅为0.075。该模型用于香兰素吸附金属离子的测试,验证了基于机器学习训练金属离子吸附能预测模型的可行性,但仍需进一步提高其泛化能力。本研究基于机器学习预测吸附能,简化预测过程、节省计算时间,可为吸附去除金属离子的理论和实验研究提供参考。

The adsorption behavior of graphene oxide and metal ions was simulated theoretically by density functional theory.In the process of training the prediction model based on the machine learning method,the missing values were processed by matrix completion method,which was widely used in the recommendation systems,and gradient boosting machine(GBM)was trained to explain the importance of factors that affect the adsorption energy.The result showed that nine properties of the adsorption,namely ionic radius,zero-point vibration energy,Mulliken charge,boiling point,dipole moment,atomic weight,molar heat capacity at constant volume(CV),spin multiplicity and bond length,were found to provide 90%importance of the cumulative adsorption energy.Then six regression methods,including support vector regression,ridge regression,random forest,extremely randomized trees,extreme gradient boosting,and light gradient boosting machine,were used to quantitatively evaluate the prediction accuracy.The results showed that machine learning could provide sufficient accuracy to predict adsorption energy.Among them,extremely randomized trees displayed the best prediction performance,with a mean square error only 0.075.Furthermore,the trained model was tested in a system of vanillin adsorbing metal ions,verifying the feasibility of training the prediction model of adsorption energy based on machine learning.But it is still necessary to be further improved.In general,this research takes the advantage of machine learning on the basis of saving experimental time to provide an instructive reference for theoretical research on metal ion removal.