Prediction of properties of metal alloy materials based on machine learning
This work addresses a time and money-saving problem for materials scientists, but it is incremental as it applies existing machine learning methods to a known bottleneck.
The paper tackled the high computational cost of density functional theory for predicting metal alloy properties by applying machine learning models, achieving accurate predictions for atomic volume, energy, and formation energy.
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.