68 citations · 119 across the 5 of their papers we have counts for
6 papers
Machine learning interatomic potential for high throughput screening and optimization of high-entropy alloys
Anup Pandey, Jonathan Gigax, Reeju Pokharel
We have developed a machine learning-based interatomic potential (MLIP) for the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high entropy alloys (HEAs). MLIPs enabled accurat…
Machine learning enabled surrogate crystal plasticity model for spatially resolved 3D orientation evolution under uniaxial tension
Anup Pandey, Reeju Pokharel
We present a novel machine learning based surrogate modeling method for predicting spatially resolved 3D microstructure evolution of polycrystalline materials under uniaxial tensil…
Evolution of amorphous carbon across densities: an inferential study
Bishal Bhattarai, Anup Pandey, D. A. Drabold
In this paper, we offer large and realistic models of amorphous carbon spanning densities from 0.95 g/cm3 to 3.5 g/cm3 . The models are designed to agree as closely as possible wit…
Ab initio model of amorphous zinc oxide (a-ZnO) and a-X_0.375 Z_0.625 O (X=Al, Ga and In)
Anup Pandey, Heath Scherich, D. A. Drabold
Density functional theory (DFT) calculations are carried out to study the structure and electronic structure of amorphous zinc oxide (a-ZnO). The models were prepared by the "melt-…
Realistic inversion of diffraction data for an amorphous solid: the case of amorphous silicon
Anup Pandey, Parthapratim Biswas, Bishal Bhattarai +1
We apply a new method "force enhanced atomic refinement" (FEAR) to create a computer model of amorphous silicon (a-Si), based upon the highly precise X-ray diffraction experiments…
Inversion of diffraction data for amorphous materials
Anup Pandey, Parthapratim Biswas, David A. Drabold
The general and practical inversion of diffraction data-producing a computer model correctly representing the material explored - is an important unsolved problem for disordered ma…