基于第一性原理计算的固溶体合金集成学习设计方法

doi:10.11896/cldb.23030089

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Abstract The first-principles method based on density functional theory (DFT) gives a more reliable calculation of the basic properties of alloys after considering the characteristics of chemical short-range orders (CSRO) of solid solutions.This work embeds the cluster-plus-glue-atom structural model describing the CSRO into first-principles calculations to obtain basic physical properties data for a series of alloys.Then a data management system was constructed to build a composition and performance database.Furthermore,multiple machine learning algorithms were used to construct prediction models for alloy composition and performance,and comparative analysis was performed.Based on this,the optimal algorithm was selected to accurately predict the performance of alloys and achieve composition design guided by performance objectives.Finally,an integrated learning design method for solid solution alloys based on first-principles calculations is integrated,and the visualization program software is developed.The method is expected to significantly improve the efficiency of the development of high-performance advanced alloy materials.

Key words： first-principle cluster structural model machine learning integrated calculation methods

Published: 10 July 2024 Online: 2024-08-01

CLC number:

TG146.8

Fund:National Foundation Strengthening Program (2021-JCJQ-ZD-030-12).

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	Articles by authors
	ZHANG Qixiang
	YUAN Junhao
	LI Zhen
	LI Wenjie
	SUN Dan
	WANG Qing
	DONG Chuang

Cite this article:

ZHANG Qixiang,YUAN Junhao,LI Zhen, et al. Integrated Learning Design Method for Solid-Solution Alloys Based on First-principles Calculations[J]. Materials Reports, 2024, 38(13): 23030089-8.

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http://www.mater-rep.com/EN/10.11896/cldb.23030089 OR http://www.mater-rep.com/EN/Y2024/V38/I13/23030089

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