- Introduction
- APS tutorial
- AIMS tutorial
- Basics
- ES tutorial
- FF/MC related tutorial
- AI tutorial
- QC tutorial
- References
- How to contribute
- Correspondence
- Funding support
- License
The JARVIS-Tools Notebooks is Collection of Jupyter/ Google-Colab notebooks to provide tutorials on various methods for materials design as well as to enhance reproducibility and transparency for scientific methods. It consists of several types of applications such as for electronic structure (ES), force-field (FF), Monte Carlo (MC), artificial intelligence (AI), quantum computation (QC) and experiments (EXP). These notebooks/similar notebooks were used for generating results in various publications as well as in JARVIS-Leaderboard. This project is a part of the NIST-JARVIS infrastructure. A few more detailed tutorial are also available at: https://jarvis-tools.readthedocs.io/en/master/tutorials.html
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- Python beginners notebook
- For absolute Beginners in ML using Python
- Silicon atomic structure and analysis example
- Analyzing_data_in_the_JARVIS_DFT_dataset
- JARVIS_Optoelectronics_Computational_screening_of_high_performance_optoelectronic_materials_using_OptB88vdW_and_TB_mBJ_formalisms
- JARVIS_TopologicalSpillage_High_throughput_Discovery_of_Topologically_Non_trivial_Materials_using_Spin_orbit_Spillage
- JARVIS_Solar_Accelerated_Discovery_of_Efficient_Solar_Cell_Materials_Using_Quantum_and_Machine_Learning_Methods
- Si_bandstructure&densityof_states
- JARVIS_Wannier90Example
- BoltztrapExample
- Making 2D heterostructures
- JARVIS_DFT_FormationEnergiesAccuracyCheck
- Downloading raw analysis data and input/output files
- JARVIS_DFT_2D_High_throughput_Identification_and_Characterization_of_Two_dimensional_Materials_using_Density_functional_theory
- JARVIS_CONVERG_Convergence_and_machine_learning_predictions_of_Monkhorst_Pack_k_points_and_plane_wave_cut_off_in_high_throughput_DFT_calculations
- JARVIS_DFPT_High_throughput_Density_Functional_Perturbation_Theory_and_Machine_Learning_Predictions_of_Infrared,_Piezoelectric_and_Dielectric_Responses
- JARVIS_TE_Data_driven_discovery_of_3D_and_2D_thermoelectric_materials
- JARVIS_ELAST_Elastic_properties_of_bulk_and_low_dimensional_materials_using_van_der_Waals_density_functional
- Get JARVIS-DFT final structures in ASE or Pymatgen format
- JARVIS_Solar_Accelerated_Discovery_of_Efficient_Solar_Cell_Materials_Using_Quantum_and_Machine_Learning_Methods
- JARVIS_TopologicalSpillage_High_throughput_Discovery_of_Topologically_Non_trivial_Materials_using_Spin_orbit_Spillage
- JARVIS_Optoelectronics_Computational_screening_of_high_performance_optoelectronic_materials_using_OptB88vdW_and_TB_mBJ_formalisms
- JARVIS_QuantumEspressoColab_Designing_High_Tc_Superconductors_with_BCS_inspired_Screening,_Density_Functional_Theory_and_Deep_learning
- JARVIS_WTBH_Database_of_Wannier_tight_binding_Hamiltonians_using_high_throughput_density_functional_theory
- Comapre_MP_JV
- ParsingWebpages(JARVIS_DFT)
- ConvexHull
- DimensionalityAndExfoliationEnergy
- Element_filter_for_JARVIS_DFT_dataset
- Run GPAW on Google-colab and calculate interface energy with jarvis-tools
- ParsingWebpages(JARVIS_DFT)
- WTBH_MagneticMats.ipynb
- JARVIS_ALIGNN_Training_example
- Simple_Machine_learning_training_example_with_CFID_descriptors
- ML_Chem_Formula_Descriptors
- AtomVision_Example
- ChemNLP example
- JARVIS_ML_LightGBM_GPUvsCPU
- JARVIS_ML_TensorFlowExample
- ALIGNN-GetTotalEnergy
- JARVIS_ML_TrainingGPU
- JARVIS_STEM_2D
- With new qiskit package version: Quantum computation and Qiskit based electronic bandstructure
- With old qiskit package version: Quantum computation and Qiskit based electronic bandstructure
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design & various other publications
For detailed instructions, please see Contribution instructions
Please report bugs as Github issues(preferred) or email to [email protected].
NIST-MGI (https://www.nist.gov/mgi).
Please see Code of conduct