Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface, Scientific 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).

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)

High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage, Scientific Reports 9, 8534 (2019)

Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods, Chem. Mater., 31, 15, 5900 (2019)

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

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

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

Computational Search for Magnetic and Non-magnetic 2D Topological Materials using Unified Spin-orbit Spillage Screening ,arXiv


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

Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics, MRS Comm., 9, 821 (2019)

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning, Nat. Comm., 10, 5316 (2019)

More details coming soon !

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