60 citations · 116 across the 6 of their papers we have counts for
6 papers
Physics-constrained 3D Convolutional Neural Networks for Electrodynamics
Alexander Scheinker, Reeju Pokharel
We present a physics-constrained neural network (PCNN) approach to solving Maxwell's equations for the electromagnetic fields of intense relativistic charged particle beams. We cre…
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…
Deformation and dislocation evolution in body-centered-cubic single- and polycrystal tantalum
Seunghyeon Lee, Hansohl Cho, Curt A. Bronkhorst +7
A physically-informed continuum crystal plasticity model is presented to elucidate the deformation mechanisms and dislocation evolution in body-centered-cubic (bcc) tantalum widely…
Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imaging
Alexander Scheinker, Reeju Pokharel
We present a novel adaptive machine-learning based approach for reconstructing three-dimensional (3D) crystals from coherent diffraction imaging (CDI). We represent the crystals us…
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…
Combining Laue diffraction with Bragg coherent diffraction imaging at 34-ID-C
Anastasios Pateras, Ross Harder, Wonsuk Cha +10
Measurement modalities in Bragg coherent diffraction imaging (BCDI) rely on finding signal from a single nanoscale crystal object, which satisfies the Bragg condition among a large…