Speaker
Description
Multi-component alloy systems based on copper (Cu), silver (Ag), titanium (Ti) and nickel (Ni) are employed in various industrial applications owing to their desirable mechanical, electrical, thermal and antimicrobial properties. Accurate prediction of mechanical properties such as melting points, hardness and modulus of elasticity is crucial for materials discovery, particularly alloy development for specific applications. In recent years, data-driven approaches have been adopted widely in materials research. This paper presents a machine learning model that utilizes the XGBoost algorithm for alloy melting point (°C), hardness (GPa) and modulus of elasticity (GPa) prediction. The model performance is based on XGBoost Regression utilizing training data sets obtained from ThermoCalc thermodynamic databases for melting points. Hardness and modulus of elasticity data was obtained by experimental data. A decision tree-based machine learning approach which combines the predictions of multiple decision trees iteratively is developed. The model is trained using gradient boosting which assigns a weight to decision trees at each iteration to minimize the loss function between predicted and theoretical values. We are able to estimate the melting points of various multi-component alloy systems accurately within 32°C of theoretical values and hardness and modulus of elasticity values within a mean absolute error percentage error of 11.7 and 11.6, respectively. The model presented herein outperforms the state of the art, and has a generalizable ability, and it can predict the mechanical properties of various alloy combinations of Cu, Ag, Ti and Ni. The ability to model intricate interactions among various elements enables the prediction of mechanical properties with higher precision across diverse materials systems. This generalized model can be used to predict the above mechanical properties for any combination of alloy systems.
