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

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...

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Autores principales: Huang, Jingtao, Xue, Jingteng, Li, Mingwei, Cheng, Yuan, Lai, Zhonghong, Hu, Jin, Zhou, Fei, Qu, Nan, Liu, Yong, Zhu, Jingchuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10608410/
https://www.ncbi.nlm.nih.gov/pubmed/37895739
http://dx.doi.org/10.3390/ma16206757
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author Huang, Jingtao
Xue, Jingteng
Li, Mingwei
Cheng, Yuan
Lai, Zhonghong
Hu, Jin
Zhou, Fei
Qu, Nan
Liu, Yong
Zhu, Jingchuan
author_facet Huang, Jingtao
Xue, Jingteng
Li, Mingwei
Cheng, Yuan
Lai, Zhonghong
Hu, Jin
Zhou, Fei
Qu, Nan
Liu, Yong
Zhu, Jingchuan
author_sort Huang, Jingtao
collection PubMed
description 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.
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spelling pubmed-106084102023-10-28 Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations Huang, Jingtao Xue, Jingteng Li, Mingwei Cheng, Yuan Lai, Zhonghong Hu, Jin Zhou, Fei Qu, Nan Liu, Yong Zhu, Jingchuan Materials (Basel) Article 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. MDPI 2023-10-19 /pmc/articles/PMC10608410/ /pubmed/37895739 http://dx.doi.org/10.3390/ma16206757 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Jingtao
Xue, Jingteng
Li, Mingwei
Cheng, Yuan
Lai, Zhonghong
Hu, Jin
Zhou, Fei
Qu, Nan
Liu, Yong
Zhu, Jingchuan
Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations
title Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations
title_full Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations
title_fullStr Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations
title_full_unstemmed Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations
title_short Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations
title_sort exploration of solid solutions and the strengthening of aluminum substrates by alloying atoms: machine learning accelerated density functional theory calculations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10608410/
https://www.ncbi.nlm.nih.gov/pubmed/37895739
http://dx.doi.org/10.3390/ma16206757
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