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

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

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

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

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

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

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

9. Density Functional Theory based Electric Field Gradient Database, Sci. Data 7, 362 (2020).

10. Computational scanning tunneling microscope image database, Sci. Data, 8, 57 (2021).

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

12. Predicting Anomalous Quantum Confinement Effect in van der Waals Materials.

13. High-throughput search for magnetic topological materials using spin-orbit spillage, machine-learning and experiments.

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

15. 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).

16. 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.

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

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

19. High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses, npj Computational Materials 6, 64 (2020).

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

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

22. Quantum Computation for Predicting Solids-state Material Properties.