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arXiv 2109.09394cond-mat.mtrl-scics.LGphysics.comp-ph
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基于机器学习的金属合金材料性能预测

Prediction of properties of metal alloy materials based on machine learning

Southwest Jiaotong University(西南交通大学)

Houchen Zuo, Yongquan Jiang, Yan Yang, Jie Hu

摘要:

密度泛函理论及其优化算法是目前材料领域计算性质的主要方法。虽然计算结果是准确的,但它需要花费大量的时间和金钱。为了缓解这个问题,我们打算使用机器学习来预测材料性能。本文利用开放的量子材料数据库,对金属合金的原子体积、原子能量和原子形成能进行了实验研究。通过传统的机器学习模型、深度学习网络和自动化机器学习,验证了机器学习在材料性能预测中的可行性。实验结果表明,机器学习可以准确地预测材料的性能。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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