[1]左厚辰,江永全*,杨燕.基于机器学习的铝合金性质预测[J].中国材料进展,2024,43(04):090-99.
　Houchen Zuo,Yongquan Jiang *,Yan Yang.Attribute Prediction of Aluminum Alloy Based on Machine Learning[J].MATERIALS CHINA,2024,43(04):090-99.
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基于机器学习的铝合金性质预测()
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中国材料进展[ISSN:1674-3962/CN:61-1473/TG]

卷:
43
期数:
2024年第04期
页码:
090-99
栏目:
出版日期:
2024-04-29

文章信息/Info

Title:
Attribute Prediction of Aluminum Alloy Based on Machine Learning
作者:
左厚辰1; 江永全*2; 杨燕2
1 西南交通大学牵引动力国家重点实验室 四川省 成都市 610031， 2 西南交通大学计算机与人工智能学院 四川省 成都市 611756
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
关键词:
合金材料; 性质预测; 人工智能;  机器学习;  自动机器学习
Keywords:
alloy material;  property prediction;  artificial intelligence;  machine learning;  automatic machine learning
文献标志码:
A
摘要:
当前材料领域对于属性的计算主体上还是以密度泛函以及它的优化算法为主，虽然计算结果准确，但需花费大量的时间和资源。近年来人工智能在材料领域应用广泛，在材料性质预测领域，不少学者引用机器学习算法进行实验，取得了不错的效果。实验针对原子平均体积、原子平均能量以及原子形成能三个性质开展，使用的数据集来自于开放量子材料数据库OQMD，通过支持向量机模型、梯度提升回归模型、自动机器学习auto_ml和AutoKeras以及基于AutoKeras最佳模型改进的深度全连接网络DNN和残差网络ResNet，验证机器学习在材料属性预测的可行性和准确性。实验结果表明，基于自动机器学习AutoKeras最佳模型改进的ResNet效果最佳，原子平均体积的最佳模型的R-Square为0.9655，MAE为0.6306*10-30m3/atom，原子平均能量的最佳模型的R-Square为0.9724，MAE为0.1466 eV/atom，原子形成能的最佳模型的R-Square为0.9880，MAE为0.0732 eV/atom。
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.
更新日期/Last Update: 2023-09-28