113 citations · 175 across the 12 of their papers we have counts for
16 papers
Benchmarking average atom potentials derived from atomic cluster expansions
Deepak Somani, Lorenzo Piersante, Anirudh Raju Natarajan
Average atom (A-atom) potentials provide a mean-field description of a chemically disordered alloy and are used to predict the properties of solid solutions without short-range ord…
Chemical site bases and average-atom potentials for the atomic cluster expansion
Lorenzo Piersante, Anirudh Raju Natarajan
Interatomic potentials are central tools in the atomistic modeling of materials. The atomic cluster expansion (ACE) parameterizes such potentials from ab initio data, conventionall…
First-principles calculations of the thermodynamic factor of multicomponent alloys
Damien K. J. Lee, Shashank Saxena, Anton Van der Ven +1
Computing non-dilute diffusion coefficients from Onsager transport coefficients of a multicomponent alloy requires the thermodynamic factor, which measures the curvature of the fre…
Cracking the case: fluctuations enhance ductility in refractory alloys
Manura Liyanage, Julia Chmielewska, Tijmen Vermeij +4
Refractory body-centered cubic (BCC) alloys are attractive candidates for structural applications at extreme temperatures, yet combining room-temperature ductility with high-temper…
pyeCE: A Python Implementation of the Embedded Cluster Expansion
Yann L. Müller, Claire A. Paetsch, Anirudh Raju Natarajan
The cluster expansion is a widely used approach for predicting the finite-temperature thermodynamics of alloys from zero-kelvin first-principles calculations, but its conventional…
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…
Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys
Gregory Teichert, Anirudh Natarajan, Anton Van der Ven +1
The free energy plays a fundamental role in descriptions of many systems in continuum physics. Notably, in multiphysics applications, it encodes thermodynamic coupling between diff…
Linking electronic structure calculations to generalized stacking fault energies in multicomponent alloys
Anirudh Raju Natarajan, Anton Van der Ven
The generalized stacking fault energy is a key ingredient to mesoscale models of dislocations. Here we develop an approach to quantify the dependence of generalized stacking fault…
Machine learning the DFT potential energy surface for inorganic halide perovskite CsPbBr
John C. Thomas, Jonathon S. Bechtel, Anirudh Raju Natarajan +1
Structural phase transitions as a function of temperature dictate the structure--functionality relationships in many technologically important materials. Harmonic Hamiltonians have…
Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions
G. H. Teichert, A. R. Natarajan, A. Van der Ven +1
The free energy of a system is central to many material models. Although free energy data is not generally found directly, its derivatives can be observed or calculated. In this wo…
A comparison of Redlich-Kister polynomial and cubic spline representations of the chemical potential in phase field computations
Gregory H. Teichert, N. S. Harsha Gunda, Shiva Rudraraju +4
Free energies play a central role in many descriptions of equilibrium and non-equilibrium properties of solids. Continuum partial differential equations (PDEs) of atomic transport,…