Collinear-spin machine learned interatomic potential for Fe7Cr2Ni alloy

被引：3

作者：

Shenoy, Lakshmi ^{[1
]}

Woodgate, Christopher D. ^{[2
]}

Staunton, Julie B. ^{[2
]}

Bartok, Albert P. ^{[1
,2
]}

Becquart, Charlotte S. ^{[3
]}

Domain, Christophe ^{[4
]}

Kermode, James R. ^{[1
]}

机构：

[1] Univ Warwick, Warwick Ctr Predict Modelling, Sch Engn, Coventry CV4 7AL, England

[2] Univ Warwick, Dept Phys, Coventry CV4 7AL, England

[3] Univ Lille, UMET Unite Mat & Transformat, Cent Lille, CNRS,INRAE,UMR 8207, F-59000 Lille, France

[4] Elect France, Dept Mat & Mecan Composants, EDF Rech & Dev, F-77250 Les Renardieres, Moret Sur Loing, France

来源：

PHYSICAL REVIEW MATERIALS | 2024年 / 8卷 / 03期

基金：

英国工程与自然科学研究理事会;

关键词：

TOTAL-ENERGY CALCULATIONS; MOLECULAR-DYNAMICS; ELASTIC-CONSTANTS; WAVE; DEPENDENCE; EFFICIENT;

D O I：

10.1103/PhysRevMaterials.8.033804

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

We have developed a machine learned interatomic potential for the prototypical austenitic steel Fe7Cr2Ni, using the Gaussian approximation potential (GAP) framework. This GAP can model the alloy's properties with close to density functional theory (DFT) accuracy, while at the same time allowing us to access larger length and time scales than expensive first -principles methods. We also extended the GAP input descriptors to approximate the effects of collinear spins (spin GAP), and demonstrate how this extended model successfully predicts structural distortions due to antiferromagnetic and paramagnetic spin states. We demonstrate the application of the spin GAP model for bulk properties and vacancies and validate against DFT. These results are a step towards modeling the atomistic origins of ageing in austenitic steels with higher accuracy.

引用

页数：14

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