Prediction of properties of metal alloy materials based on machine learning

TL;DR

This paper demonstrates that machine learning models can accurately predict properties of metal alloy materials, offering a faster and cost-effective alternative to traditional density functional theory calculations.

Contribution

It explores the application of various machine learning techniques to predict metal alloy properties, validating their effectiveness with experimental results.

Findings

Machine learning accurately predicts atomic volume, energy, and formation energy.

Deep learning and automated machine learning improve prediction accuracy.

Machine learning reduces computational costs compared to traditional methods.

Abstract

Density functional theory and its optimization algorithm are the main methods to calculate the properties in the field of materials. Although the calculation results are accurate, it costs a lot of time and money. In order to alleviate this problem, we intend to use machine learning to predict material properties. In this paper, we conduct experiments on atomic volume, atomic energy and atomic formation energy of metal alloys, using the open quantum material database. Through the traditional machine learning models, deep learning network and automated machine learning, we verify the feasibility of machine learning in material property prediction. The experimental results show that the machine learning can predict the material properties accurately.