Random forest incorporating ab-initio calculations for corrosion rate prediction with small sample Al alloys data

• Published in: Npj Materials degradation Vol. 6; no. 1; pp. 1 - 10
• Main Authors: Ji, Yucheng, Li, Ni, Cheng, Zhanming, Fu, Xiaoqian, Ao, Min, Li, Menglin, Sun, Xiaoguang, Chowwanonthapunya, Thee, Zhang, Dawei, Xiao, Kui, Ren, Jingli, Dey, Poulumi, Li, Xiaogang, Dong, Chaofang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.10.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1023/1026; 639/301/1034/1037; 639/705; Accuracy; Aluminum base alloys; Big Data; Chemistry and Materials Science; Corrosion; Corrosion and Coatings; Corrosion rate; Datasets; Electrochemistry; Engineering; Experiments; Intermetallic compounds; Machine learning; Materials Science; Materials selection; Prediction models; Sensors; Structural Materials; Tribology; Work functions

Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large...