Chemical short-range order(SRO)affects the distribution of elements throughout the solid-solution phase of metallic alloys,thereby modifying the background against which microstructural evolution occurs.Investigating ...Chemical short-range order(SRO)affects the distribution of elements throughout the solid-solution phase of metallic alloys,thereby modifying the background against which microstructural evolution occurs.Investigating such chemistry–microstructure relationships requires atomistic models that act at the appropriate length scales while capturing the intricacies of chemical bonds leading to SRO.Here,we consider various approaches for the construction of training data sets for machine learning potentials(MLPs)for CrCoNi and evaluate their performance in capturing SRO and its effects on materials quantities of relevance for mechanical properties,such as stacking-fault energy and phase stability.It is demonstrated that energy accuracy on test sets often does not correlate with accuracy in capturing material properties,which is fundamental in enabling large-scale atomistic simulations of metallic alloys with high physical fidelity.Based on this analysis,we systematically derive design principles for the rational construction of MLPs that capture SRO in the crystal and liquid phases of alloys.展开更多
基金supported by the MathWorks Ignition Fund,MathWorks Engineering Fellowship Fund,and the Portuguese Foundation for International Cooperation in Science,Technology and Higher Education in the MIT—Portugal Programsupported by the Research Support Committee Funds from the School of Engineering at the Massachusetts Institute of Technology+1 种基金This work used the Expanse supercomputer at the San Diego Supercomputer Center through allocation MAT210005 from the Advanced Cyber Infrastructure Coordination Ecosystem:Services&Support(ACCESS)program,which is supported by National Science Foundation grants#2138259,#2138286,#2138307,#2137603,and#2138296the Extreme Science and Engineering Discovery Environment(XSEDE),which was supported by National Science Foundation grant number#1548562.
文摘Chemical short-range order(SRO)affects the distribution of elements throughout the solid-solution phase of metallic alloys,thereby modifying the background against which microstructural evolution occurs.Investigating such chemistry–microstructure relationships requires atomistic models that act at the appropriate length scales while capturing the intricacies of chemical bonds leading to SRO.Here,we consider various approaches for the construction of training data sets for machine learning potentials(MLPs)for CrCoNi and evaluate their performance in capturing SRO and its effects on materials quantities of relevance for mechanical properties,such as stacking-fault energy and phase stability.It is demonstrated that energy accuracy on test sets often does not correlate with accuracy in capturing material properties,which is fundamental in enabling large-scale atomistic simulations of metallic alloys with high physical fidelity.Based on this analysis,we systematically derive design principles for the rational construction of MLPs that capture SRO in the crystal and liquid phases of alloys.
