Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations

Jingtao Huang; Jingteng Xue; Mingwei Li; Yuan Cheng; Zhonghong Lai; Jin Hu; Fei Zhou; Nan Qu; Yong Liu; Jingchuan Zhu

doi:10.3390/ma16206757

Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations

Materials (Basel). 2023 Oct 19;16(20):6757. doi: 10.3390/ma16206757.

Authors

Jingtao Huang¹, Jingteng Xue¹, Mingwei Li², Yuan Cheng³, Zhonghong Lai⁴, Jin Hu¹, Fei Zhou⁵, Nan Qu¹, Yong Liu^{1

2}, Jingchuan Zhu¹

Affiliations

¹ School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
² National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin 150001, China.
³ National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150001, China.
⁴ Center for Analysis, Measurement and Computing, Harbin Institute of Technology, Harbin 150001, China.
⁵ State Key Laboratory for Environment-Friendly Energy Materials, School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China.

Abstract

In this paper, we studied the effects of a series of alloying atoms on the stability and micromechanical properties of aluminum alloy using a machine learning accelerated first-principles approach. In our preliminary work, high-throughput first-principles calculations were explored and the solution energy and theoretical stress of atomically doped aluminum substrates were extracted as basic data. By comparing five different algorithms, we found that the Catboost model had the lowest RMSE (0.24) and lowest MAPE (6.34), and this was used as the final prediction model to predict the solid solution strengthening of the aluminum matrix by the elements. Calculations show that alloying atoms such as K, Na, Y and Tl are difficult to dissolve in the aluminum matrix, whereas alloy atoms like Sc, Cu, B, Zr, Ni, Ti, Nb, V, Cr, Mn, Mo, and W exerted a strengthening influence. Theoretical studies on solid solutions and the strengthening effect of various alloy atoms in an aluminum matrix can offer theoretical guidance for the subsequent selection of suitable alloy elements. The theoretical investigation of alloy atoms in an aluminum matrix unveils the fundamental aspects of the solution strengthening effect, contributing significantly to the expedited development of new aluminum alloys.

Keywords: aluminum substrate; density function theory; explainable machine learning; mechanical properties; single atoms.

Grants and funding

This research was funded by the Science Foundation of National Key Laboratory of Science and Technology on Advanced Composites in Special Environments.