Abstract
Multi-principal element alloys (MPEAs) are a new class of alloys that consist of many principal elements randomly distributed on a crystal lattice. The random presence of many elements lends large variations in the point defect formation and migration energies even within a given alloy composition. Compounded by the fact that there could be exponentially large number of MPEA compositions, there is a major computational challenge to capture complete point-defect energy phase-space in MPEAs. In this work, we present a machine learning based framework in which the point defect energies in MPEAs are predicted from a database of their constituent binary alloys. We demonstrate predictions of vacancy migration and formation energies in face centered cubic ternary, quaternary and quinary alloys in Ni-Fe-Cr-Co-Cu system. A key benefit of building this framework based on the database of binary alloys is that it enables defect-energy predictions in alloy compositions that may be unearthed in future. Furthermore, the methodology enables identifying the impact of a given alloying element on the defect energies thereby enabling design of alloys with tailored defect properties.
Highlights
Multi-principal element alloys (MPEAs) are a new class of alloys that consist of many principal elements randomly distributed on a crystal lattice
In Cu60 alloy composition, Cu is 60 at%, whereas Ni, Fe, Cr, and Co are in 10 at% each
We have developed a machine learning based framework to predict vacancy migration and formation energies in ternary, quaternary and quinary concentrated alloys from the database built from their constituent binary alloys
Summary
Multi-principal element alloys (MPEAs) are a new class of alloys that consist of many principal elements randomly distributed on a crystal lattice. There are large variations in defect energies even within a given alloy composition (Del Rio et al, 2011; Piochaud et al, 2014; Zhang et al, 2015, 2017; Zhao et al, 2016, 2018; Li et al, 2019; Guan et al, 2020; Arora et al, 2021). This is in contrast to essentially a single defect energy value in conventional and/or dilute alloys. Using density functional theory (DFT) calculations, Guan et al (Guan et al, 2020)
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