kfgarrity / jarvis-tools-notebooks

A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/

Table of Contents

• Introduction
• Basics
• ES tutorial
• FF/MC related tutorial
• JARVIS-School
• AI tutorial
• QC tutorial
• References
• How to contribute
• Correspondence
• Funding support
• License

JARVIS-Tools Notebooks (Introduction)

The JARVIS-Tools Notebooks is a collection of Jupyter/ Google-Colab notebooks to provide tutorials on various methods for materials design. 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). This project is a part of the NIST-JARVIS infrastructure. A few more detailed tutorial are also available at: JARVIS-Tools.

Basics

1. Python beginners notebook
2. For absolute Beginners in ML using Python
3. Silicon atomic structure and analysis example

Electronic structure

1. Analyzing_data_in_the_JARVIS_DFT_dataset
2. Analyzing_data_in_the_JARVIS_Leaderboard
3. Basic quantum espresso run
4. JARVIS_Optoelectronics_Computational_screening_of_high_performance_optoelectronic_materials_using_OptB88vdW_and_TB_mBJ_formalisms
5. JARVIS_TopologicalSpillage_High_throughput_Discovery_of_Topologically_Non_trivial_Materials_using_Spin_orbit_Spillage
6. JARVIS_Solar_Accelerated_Discovery_of_Efficient_Solar_Cell_Materials_Using_Quantum_and_Machine_Learning_Methods
7. Si_bandstructure&densityof_states
8. JARVIS_Wannier90Example
9. BoltztrapExample
10. Making 2D heterostructures
11. JARVIS_DFT_FormationEnergiesAccuracyCheck
12. Downloading raw analysis data and input/output files
13. JARVIS_DFT_2D_High_throughput_Identification_and_Characterization_of_Two_dimensional_Materials_using_Density_functional_theory
14. JARVIS_CONVERG_Convergence_and_machine_learning_predictions_of_Monkhorst_Pack_k_points_and_plane_wave_cut_off_in_high_throughput_DFT_calculations
15. JARVIS_DFPT_High_throughput_Density_Functional_Perturbation_Theory_and_Machine_Learning_Predictions_of_Infrared,_Piezoelectric_and_Dielectric_Responses
16. JARVIS_TE_Data_driven_discovery_of_3D_and_2D_thermoelectric_materials
17. JARVIS_ELAST_Elastic_properties_of_bulk_and_low_dimensional_materials_using_van_der_Waals_density_functional
18. Get JARVIS-DFT final structures in ASE or Pymatgen format
19. JARVIS_Solar_Accelerated_Discovery_of_Efficient_Solar_Cell_Materials_Using_Quantum_and_Machine_Learning_Methods
20. JARVIS_TopologicalSpillage_High_throughput_Discovery_of_Topologically_Non_trivial_Materials_using_Spin_orbit_Spillage
21. JARVIS_Optoelectronics_Computational_screening_of_high_performance_optoelectronic_materials_using_OptB88vdW_and_TB_mBJ_formalisms
22. JARVIS_QuantumEspressoColab_Designing_High_Tc_Superconductors_with_BCS_inspired_Screening,_Density_Functional_Theory_and_Deep_learning
23. JARVIS_WTBH_Database_of_Wannier_tight_binding_Hamiltonians_using_high_throughput_density_functional_theory
24. Comapre_MP_JV
25. ParsingWebpages(JARVIS_DFT)
26. ConvexHull
27. DimensionalityAndExfoliationEnergy
28. Element_filter_for_JARVIS_DFT_dataset
29. Run GPAW on Google-colab and calculate interface energy with jarvis-tools
30. ParsingWebpages(JARVIS_DFT)
31. WTBH_MagneticMats.ipynb

Force-field

1. JARVIS_LAMMPS
2. MLFF SNAP training
3. ALIGNN-FF for energy and forces
4. Analyzing_MOF_datasets

Artificial intelligence/Machine learning

1. CIF To Graph_example
2. JARVIS_ALIGNN_Training_example
3. Simple_Machine_learning_training_example_with_CFID_descriptors
4. ML_Chem_Formula_Descriptors
5. AtomVision_Leaderboard_Example
6. AtomVision_Example
7. ChemNLP example
8. ChemNLP HuggingFace example
9. JARVIS_ML_LightGBM_GPUvsCPU
10. JARVIS_ML_TensorFlowExample
11. ALIGNN-GetTotalEnergy
12. ALIGNN-PhononDos
13. JARVIS_ML_TrainingGPU
14. JARVIS_STEM_2D
15. JARVIS_Leaderboard_ALIGNN
16. JARVIS_Leaderboard_KGCNN
17. JARVIS_Leaderboard_MatMiner
18. ALIGNN-FF cubic relaxer
19. Open catalyst project load model
20. Vacancy formation ML
21. ALIGNN-FF catalyst adsorption energy

Quantum computation

1. With new qiskit package version: Quantum computation and Qiskit based electronic bandstructure
2. With old qiskit package version: Quantum computation and Qiskit based electronic bandstructure

JARVIS-School

More info

APS2023 tutorial

More info

AIMS2022 tutorial

More info

References

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design & various other publications

How to contribute

For detailed instructions, please see Contribution instructions

Correspondence

Please report bugs as Github issues(preferred) or email to [e-mail].

Funding support

NIST-MGI (https://www.nist.gov/mgi).

Code of conduct

Please see Code of conduct

License

NIST License