3 citations · 4 across the 4 of their papers we have counts for
4 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…
Feature engineering for microstructure-property mapping in organic photovoltaics
Sepideh Hashemi, Baskar Ganapathysubramanian, Stephen Casey +2
Linking the highly complex morphology of organic photovoltaic (OPV) thin films to their charge transport properties is critical for achieving high performance material system that…
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…
Materials knowledge system for nonlinear composites
Marat I. Latypov, Laszlo S. Toth, Surya R. Kalidindi
In this contribution, we present a new Materials Knowledge System framework for microstructure-sensitive predictions of effective stress--strain responses in composite materials. T…