Info

Title:

Attribute Prediction of Aluminum Alloy Based on Machine Learning

Author(s):

Houchen Zuo 1;  Yongquan Jiang *2;  Yan Yang 2

1.State key labratory of traction power, Southwest Jiaotong University, Chengdu 610031, China, 2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China

Keywords:

alloy material;  property prediction;  artificial intelligence;  machine learning;  automatic machine learning

CLC:

PACS:

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DOI:

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DocumentCode:

Abstract:

At present, the calculation of properties in the field of materials is mainly based on density functional theory and its optimization algorithm. Although the calculation results are accurate, it costs a lot of time and source. In recent years, artificial intelligence is widely used in the field of materials. In the field of material property prediction, many scholars use machine learning algorithm to experiment and achieve good results. The experiment is carried out for the three properties of average atomic volume, average atomic energy and atomic formation energy. The data set used is the open quantum material database. Through support vector machine model, gradient boosting regression model, automatic machine learning auto_ml and AutoKeras, deep network fully connected network and residual network, experiments verify the feasibility and accuracy of machine learning in material attribute prediction. The experimental results show that the improved models based on the best model provided by automatic machine learning AutoKeras have the best effect. The R-Square of the best model of atomic average volume is 0.9655, MAE is 0.6306 * 10-30m3/atom. The R-square of the best model of atomic average energy is 0.9724, MAE is 0.1466 eV / atom. The R-square of the best model of atomic formation energy is 0.9880, MAE is 0.0732 eV / atom. The development of machine learning algorithm in the field of materials can solve the consumption of time and financial resources caused by the huge data calculation of traditional mathematical models. Moreover, with the improvement of the accuracy of the prediction results, the prediction algorithm can guide the experiment to a certain extent in the future and can avoid a lot of redundant and useless work. It will be a trend in the field of materials in the future to predict the properties of materials in advance and dig out the properties of unknown alloys through machine learning.