6 papers
Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials
Xin Liu, Anirudh Raju Natarajan
Multicomponent transition metal carbides are promising for extreme-environment applications, but identifying compositions that are both synthesizable and hard remains challenging.…
Diffusion coefficients of multi-principal element alloys from first principles
Damien K. J. Lee, Anirudh Raju Natarajan
Vacancy-mediated diffusion in multi-principal element alloys (MPEAs) remains poorly understood. Existing computational methods face challenges in connecting electronic structure to…
Machine learning interatomic potentials for solid-state precipitation
Lorenzo Piersante, Anirudh Raju Natarajan
Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformat…
Thermodynamic and electronic properties of rutile SnGeO alloys from first principles
Yann L. Müller, Alp Umut Kurbay, Xiao Zhang +2
Rutile SnGeO alloys are promising materials for high-power electronic applications due to their dopability and tunable ultra-wide band gaps. We use first-principl…
Modeling the Equilibrium Vacancy Concentration in Multi-Principal Element Alloys from First-Principles
Damien K. J. Lee, Yann L. Müller, Anirudh Raju Natarajan
Multi-principal element alloys (MPEAs), also known as high-entropy alloys, have garnered significant interest across many applications due to their exceptional properties. Equilibr…
Constructing multicomponent cluster expansions with machine-learning and chemical embedding
Yann L. Müller, Anirudh Raju Natarajan
Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model ma…