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Publications

JARVIS-Overview

[1. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design, npj Computational Materials 6, 173 (2020).](https://www.nature.com/articles/s41524-020-00440-1)

[2. Recent progress in the JARVIS infrastructure for next-generation data-driven materials design, arXiv (2023).](https://arxiv.org/abs/2305.11842)

[3. Large Scale Benchmark of Materials Design Methods, arXiv(2023).](https://arxiv.org/abs/2306.11688)

JARVIS-FF

[4. 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)

[5. 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)

[6. 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)

[7. Computational Screening of High-performance Optoelectronic Materials using OptB88vdW and TBmBJ Formalisms, Scientific Data 5, 180082 (2018).](https://www.nature.com/articles/sdata201882)

[8. 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)

[9. 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)

[10. 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)

[11. Density Functional Theory based Electric Field Gradient Database, Sci. Data 7, 362 (2020).](https://www.nature.com/articles/s41597-020-00707-8)

[12. Computational scanning tunneling microscope image database, Sci. Data, 8, 57 (2021).](https://www.nature.com/articles/s41597-021-00824-y)

[13. Database of Wannier Tight-binding Hamiltonians using High-throughput Density Functional Theory, Sci. Data](https://www.nature.com/articles/s41597-021-00885-z)

[14. Predicting Anomalous Quantum Confinement Effect in van der Waals Materials, Phys. Rev. Mat.](https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.5.054602)

[15. High-throughput search for magnetic topological materials using spin-orbit spillage, machine-learning and experiments, Phys. Rev. B](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.103.155131)

[16. Density functional theory-based electric field gradient database, Sci. Data](https://www.nature.com/articles/s41597-020-00707-8)

[17. High-throughput DFT-based discovery of next generation two-dimensional (2D) superconductors](https://pubs.acs.org/doi/full/10.1021/acs.nanolett.2c04420)

[18. A systematic DFT+U and Quantum Monte Carlo benchmark of magnetic two-dimensional (2D) CrX (X = I, Br, Cl, F)](https://pubs.acs.org/doi/abs/10.1021/acs.jpcc.2c06733)

[19. 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)

[28. 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)

[21. 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)

[22. 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)

[23. Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods, Chem. Mater., 31, 5900 (2019).](https://pubs.acs.org/doi/10.1021/acs.chemmater.9b02166)

[24. High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses, npj Computational Materials 6, 64 (2020).](https://www.nature.com/articles/s41524-020-0337-2)

[25. Data-driven Discovery of 3D and 2D Thermoelectric Materials, J. Phys.: Cond. Matt.](https://iopscience.iop.org/article/10.1088/1361-648X/aba06b/meta)

[26. 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)

[27. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning, Nature Commun.](https://www.nature.com/articles/s41467-019-13297-w)

[28. Atomistic Line Graph Neural Network for Improved Materials Property Predictions, npj Computational Materials 7, 1 (2021)](https://www.nature.com/articles/s41524-021-00650-1)

[29. Recent advances and applications of deep learning methods in materials science, npj Computational Materials 8, 1 (2022)](https://www.nature.com/articles/s41524-022-00734-6)

[30. Graph neural network predictions of metal organic framework CO2 adsorption properties, Comp. Mat. Sci., 210, 111388 (2022)](https://www.sciencedirect.com/science/article/pii/S092702562200163X)

[31. Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures](https://link.springer.com/article/10.1007/s40192-022-00258-3)

[32. Uncertainty Prediction for Machine Learning Models of Material Properties](https://pubs.acs.org/doi/abs/10.1021/acsomega.1c03752)

[33. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data](https://www.nature.com/articles/s41467-021-26921-5)

[34. Prediction of the Electron Density of States for Crystalline Compounds with Atomistic Line Graph Neural Networks (ALIGNN)](https://link.springer.com/article/10.1007/s11837-022-05199-y)

[35. Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning](https://arxiv.org/abs/2205.00060)

[36. Rapid Prediction of Phonon Structure and Properties using an Atomistic Line Graph Neural Network (ALIGNN)](https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.7.023803)

[37. Unified Graph Neural Network Force-field for the Periodic Table](https://pubs.rsc.org/en/content/articlehtml/2023/dd/d2dd00096b)

[38. AtomVision: A machine vision library for atomistic images](https://pubs.acs.org/doi/full/10.1021/acs.jcim.2c01533)

[39. ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data](https://arxiv.org/abs/2209.08203)

[40. A critical examination of robustness and generalizability of machine learning prediction of materials properties](https://www.nature.com/articles/s41524-023-01012-9)

[41. Inverse design of next-generation superconductors using data-driven deep generative models](https://pubs.acs.org/doi/10.1021/acs.jpclett.3c01260)

[42. Quantum Computation for Predicting Electron and Phonon Properties of Solids., J. Phys.: Cond. Matt.](https://iopscience.iop.org/article/10.1088/1361-648X/ac1154)

[43. Fast and Accurate Prediction of Material Properties with Three-Body Tight-Binding Model for the Periodic Table](https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.7.044603)