9 citations · 13 across the 5 of their papers we have counts for
12 papers · 1 filter
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)
Prathik R Kaundinya, Kamal Choudhary, Surya R. Kalidindi
Machine learning (ML) based models have greatly enhanced the traditional materials discovery and design pipeline. Specifically, in recent years, surrogate ML models for material pr…
Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds
Prathik R. Kaundinya, Kamal Choudhary, Surya R. Kalidindi
In this study, we present a novel approach along with the needed computational strategies for efficient and scalable feature engineering of the crystal structure in compounds of di…
OPTIMADE, an API for exchanging materials data
Casper W. Andersen, Rickard Armiento, Evgeny Blokhin +53
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible an…
Quantum Computation for Predicting Electron and Phonon Properties of Solids
Kamal Choudhary
Quantum chemistry is one of the most promising near-term applications of quantum computers. Quantum algorithms such as variational quantum eigen solver (VQE) and variational quantu…
Database of Wannier Tight-binding Hamiltonians using High-throughput Density Functional Theory
Kevin F. Garrity, Kamal Choudhary
We develop a computational workflow for high-throughput Wannierization of density functional theory (DFT) based electronic band structure calculations. We apply this workflow to 17…
Density Functional Theory based Electric Field Gradient Database
Kamal Choudhary, Jaafar N. Ansari, Igor I. Mazin +1
The deviation of the electron density around the nuclei from spherical symmetry determines the electric field gradient (EFG), which can be measured by various types of spectroscopy…