Publications.rst

• JARVIS-overview:

  1. JARVIS: An Integrated Infrastructure for Data-driven Materials Design. https://arxiv.org/abs/2007.01831

• JARVIS-FF:

  2. Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface, Nature: Sci Data. 4, 160125 (2017). https://www.nature.com/articles/sdata2016125
  3. High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields, J. Phys. Cond. Matt. 30, 395901(2018). http://iopscience.iop.org/article/10.1088/1361-648X/aadaff/meta

• JARVIS-DFT:

  4. High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory, Scientific Reports 7, 5179 (2017). https://www.nature.com/articles/s41598-017-05402-0
  5. Computational Screening of High-performance Optoelectronic Materials using OptB88vdW and TBmBJ Formalisms, Scientific Data 5, 180082 (2018). https://www.nature.com/articles/sdata201882
  6. Elastic properties of bulk and low-dimensional materials using van der Waals density functional, Phys. Rev. B, 98, 014107 (2018). https://journals.aps.org/prb/abstract/10.1103/PhysRevB.98.014107
  7. High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage, Nature: Sci. Rep. 9, 8534,(2019), https://www.nature.com/articles/s41598-019-45028-y
  8. Computational Search for Magnetic and Non-magnetic 2D Topological Materials using Unified Spin-orbit Spillage Screening, npj Comp. Mat., 6, 49 (2020). https://www.nature.com/articles/s41524-020-0319-4 .
  9. Density Functional Theory based Electric Field Gradient Database. https://arxiv.org/abs/2005.09255 .
  10. Database of Wannier Tight-binding Hamiltonians using High-throughput Density Functional Theory. https://arxiv.org/abs/2007.01205 .

• JARVIS-ML:

  11. Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape, Phys. Rev. Mat., 2, 083801 (2018). https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.2.083801
  12. 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). https://www.sciencedirect.com/science/article/pii/S0927025619300813?via%3Dihub
  13. 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. https://doi.org/10.1557/mrc.2019.95
  14. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning, Nature Comm., 10, 1, (2019). https://www.nature.com/articles/s41467-019-13297-w
  15. Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods, Chem. Mater., https://pubs.acs.org/doi/10.1021/acs.chemmater.9b02166
  16. High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses, https://www.nature.com/articles/s41524-020-0337-2.
  17. Data-driven Discovery of 3D and 2D Thermoelectric Materials , https://iopscience.iop.org/article/10.1088/1361-648X/aba06b/meta.
  18. Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning. https://arxiv.org/abs/2004.03025 .
  19. Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy, https://arxiv.org/abs/1912.09027