Accelerated exploration of high-performance multi-principal element alloys: data-driven high-throughput calculations and active learning method

Zhigang Ding; Junjun Zhou; Piao Yang; Haoran Sun; Ji-Chang Ren; Yonghao Zhao; Wei Liu

doi:10.1080/21663831.2023.2215826

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Accelerated exploration of high-performance multi-principal element alloys: data-driven high-throughput calculations and active learning method

Report | 更新时间：2026-08-19

- Accelerated exploration of high-performance multi-principal element alloys: data-driven high-throughput calculations and active learning method
- Materials Research Letters Vol. 11, Issue 8, Pages: 670-677(2023)
- 作者机构：
  
  1. a Nano and Heterogeneous Materials Center School of Materials Science and Engineering Nanjing University of Science and Technology
  2. a Nano and Heterogeneous Materials Center School of Materials Science and Engineering Nanjing University of Science and Technology Nanjing People’s Republic of China
  3. b State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied Chemistry Chinese Academy of Sciences
- 作者简介：
- 基金信息：
- DOI：10.1080/21663831.2023.2215826
  CLC：
- Online First：28 May 2023，
  
  Published：2023
- 稿件说明：
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Zhigang Ding, Junjun Zhou, Piao Yang, et al. Accelerated exploration of high-performance multi-principal element alloys: data-driven high-throughput calculations and active learning method[J]. Materials Research Letters, 2023, 11(8): 670-677.
DOI：

Zhigang Ding, Junjun Zhou, Piao Yang, et al. Accelerated exploration of high-performance multi-principal element alloys: data-driven high-throughput calculations and active learning method[J]. Materials Research Letters, 2023, 11(8): 670-677. DOI： 10.1080/21663831.2023.2215826.

摘要

Abstract

We propose an active learning guided density functional theory calculation framework for rapid screening of multi-principal element alloys (MPEAs) with superior mechanical properties. Using this framework

we fast construct the datasets of the bulk modulus (

)

shear modulus (

)

yield strength

and Pugh’s ratio of 12

698 noble metal MPEAs. These datasets were obtained with density functional theory prediction accuracy (

= 0.98 and 0.96 for

and

respectively) based on active learning guided 120 DFT calculated data. Analysis of the dataset shows that Ni and Au would enhance the yield strength and the toughness of these noble MPEAs

respectively.

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Related Institution

a Multidisciplinary Research Center – Cardinal Stefan Wyszynski University in Warsaw

a Multidisciplinary Research Center – Cardinal Stefan Wyszynski University in Warsaw Warsaw Poland

b Institute of High Pressure Physics Polish Academy of Sciences

c PSL Research University Chimie ParisTech CNRS Institut de Recherche de Chimie Paris

a State Key Laboratory of Metastable Materials Science and Technology & School of Materials Science and Engineering Yanshan University

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