AI-accelerated materials informatics method for the discovery of ductile alloys

被引:0
作者
I. Novikov
O. Kovalyova
A. Shapeev
M. Hodapp
机构
[1] Center for Artificial Intelligence Technology,Skolkovo Institute of Science and Technology (Skoltech)
[2] Moscow Institute of Physics and Technology (MIPT),undefined
[3] Materials Center Leoben Forschung GmbH (MCL),undefined
来源
Journal of Materials Research | 2022年 / 37卷
关键词
Materials informatics; Moment tensor potential; Active learning; Random alloy; Ductility; Average-atom potential;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:3491 / 3504
页数:13
相关论文
共 217 条
[21]  
Behler J(2017)Active learning of linearly parametrized interatomic potentials Comput. Mater. Sci. 140 148-358
[22]  
Parrinello M(2019)Accelerating high-throughput searches for new alloys with active learning of interatomic potentials Comput. Mater. Sci. 156 174-176
[23]  
Bartók AP(2020)Ductile and brittle crack-tip response in equimolar refractory high-entropy alloys Acta Mater. 189 104389-1035
[24]  
Payne MC(2021)A ductility criterion for bcc high entropy alloys J. Mech. Phys. Solids 152 144113-271
[25]  
Kondor R(2004)Misfit-energy-increasing dislocations in vapor-deposited CoFe/NiFe multilayers Phys. Rev. B 69 116800-10336
[26]  
Csányi G(2021)Screening of generalized stacking fault energies, surface energies and intrinsic ductile potency of refractory multicomponent alloys Acta Mater. 210 731-undefined
[27]  
Thompson A(2020)Performance and cost assessment of machine learning interatomic potentials J. Phys. Chem. A 124 558-undefined
[28]  
Swiler L(1993)Ab initio molecular dynamics for liquid metals Phys. Rev. B 47 14251-undefined
[29]  
Trott C(1994)Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium Phys. Rev. B 49 15-undefined
[30]  
Foiles S(1996)Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set Comput. Mater. Sci. 6 11169-undefined