A database that captures the effects of high-entropy material compositions
on adsorption energies and active sites for ammonia decomposition. (功能性高熵材料資料庫)
The data in the database is created by density functional theory calculations and machine learning, combining genetic algorithms with deep learning of artificial neural networks. The learning process is repeated 100,000 times, and the root mean square (RMS) error is less than 0.02.
資料庫中的數據是以密度泛函理論計算和機器學習方式，經結合基因演算與人工神經網絡的深度學習所建立。學習程序為100,000次，均方根（RMS）誤差小於0.02。