25 citations · 30 across the 5 of their papers we have counts for
5 papers
Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality
Sazzad Hossain, Ponkrshnan Thiagarajan, Shashank Pathrudkar +4
Machine learning (ML) models for electronic structure typically rely on large datasets generated by computationally expensive Kohn-Sham density functional theory calculations, as i…
Electronic structure prediction of medium and high entropy alloys across composition space
Shashank Pathrudkar, Stephanie Taylor, Abhishek Keripale +6
We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated allo…
Electronic Structure Prediction of Multi-million Atom Systems Through Uncertainty Quantification Enabled Transfer Learning
Shashank Pathrudkar, Ponkrshnan Thiagarajan, Shivang Agarwal +2
The ground state electron density -- obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations -- contains a wealth of material information, making its prediction v…
Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain
Shashank Pathrudkar, Hsuan Ming Yu, Susanta Ghosh +1
We present a machine learning based model that can predict the electronic structure of quasi-one-dimensional materials while they are subjected to deformation modes such as torsion…
An Interpretable Machine Learning Model for Deformation of Multi-Walled Carbon Nanotubes
Upendra yadav, Shashank Pathrudkar, Susanta Ghosh
We present a novel interpretable machine learning model to accurately predict complex rippling deformations of Multi-Walled Carbon Nanotubes(MWCNTs) made of millions of atoms. Atom…