Publications

JARVIS-overview

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design.

Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface, Nature: Sci Data. 4, 160125 (2017).

High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields, J. Phys. Cond. Matt. 30, 395901(2018).

High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory, Scientific Reports 7, 5179 (2017).

Computational Screening of High-performance Optoelectronic Materials using OptB88vdW and TBmBJ Formalisms, Scientific Data 5, 180082 (2018).

Elastic properties of bulk and low-dimensional materials using van der Waals density functional, Phys. Rev. B, 98, 014107 (2018).

High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage, Nature: Sci. Rep. 9, 8534,(2019).

Computational Search for Magnetic and Non-magnetic 2D Topological Materials using Unified Spin-orbit Spillage Screening, npj Comp. Mat., 6, 49 (2020).

Density Functional Theory based Electric Field Gradient Database.

Database of Wannier Tight-binding Hamiltonians using High-throughput Density Functional Theory.

Predicting Anomalous Quantum Confinement Effect in van der Waals Materials.

Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape, Phys. Rev. Mat., 2, 083801 (2018).

Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations, Comp. Mat. Sci. 161, 300 (2019).

Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics, MRS Comm., 1-18, 2019.

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning, Nature Comm., 10, 1, (2019).

Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods, Chem. Mater., 2019

High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses.

Data-driven Discovery of 3D and 2D Thermoelectric Materials

Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning.

Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy