Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Abstract This study employs machine learning (ML) models such as Decision Tree (DT), Extra Tree (ET), K-Nearest Neighbors (KNN), Support Vector Regressor (SVR) and Lasso to predict how Ta and Nb content effects the mechanical behavior of the CoCrFeNi HEAs at diverse compositions. ET and KNN models developed as the most effective forecasters, accomplishing high R2 values of 0.985 and 0.984 respectively.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.