jarvis-tools-notebooks

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jarvis-tools-notebooks

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A Google-Colab Notebook Collection for Materials Design: https://atomgpt.org/

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# Table of Contents
* [Introduction](#intro)
* [Mini-course](#mini)
* [Basics](#basics)
* [ES tutorial](#es)
* [FF/MC related tutorial](#es)
* [JARVIS-School](#school)
* [AI tutorial](#ai)
* [QC tutorial](#qc)
* [EXP tutorial](#exp)
* [NanoHub tutorial](#nanohub)
* [References](#refs)
* [How to contribute](#contrib)
* [Correspondence](#corres)
* [Funding support](#fund)
* [License](#license)

<a name="intro"></a>
# 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](https://jarvis.nist.gov/) infrastructure. A few more detailed tutorial are also available at: [JARVIS-Tools](https://jarvis-tools.readthedocs.io/en/master/tutorials.html).

Slides: [SlideShare](https://www.slideshare.net/KAMALCHOUDHARY4) [https://github.com/knc6/slides](https://github.com/knc6/slides)

📺 YouTube videos: [https://www.youtube.com/@dr_k_choudhary](https://www.youtube.com/@dr_k_choudhary)

<a name="mini"></a>
# Mini-course collection

Later you'll find a large collection of notebooks related to JARVIS/Materials design. Here are a handful selected notebooks to get a quick start. These were also presented at the [NIST-AIMS workshop](http://jarvis.nist.gov/events/aims).
### JARVIS-DFT:

1. [Analyzing_data_in_the_JARVIS_DFT_dataset](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Analyzing_data_in_the_JARVIS_DFT_dataset.ipynb)
2. [Basic quantum espresso run](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_QuantumEspressoColab_Basic_Example.ipynb)

### ALIGNN:

3. [Basic_ALIGNN](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/alignn_jarvis_leaderboard.ipynb)

### AtomGPT:

4. [AtomGPT_example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/atomgpt_example.ipynb)

5. [DiffractGPT_example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/DiffractGPT_example.ipynb)

6. [MicroscopyGPT_example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/MicroscopyGPT.ipynb)

### Machine Learning Force Fields:
7. ([open in colab](https://colab.research.google.com/github/usnistgov/aims_workshop/blob/main/tutorials/intro-gnn-potentials.ipynb))

### CHIPS-FF:

8. [CHIPS_FF_example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/chipsff_example.ipynb)

### Scaling test:

9. [Scaling ALIGNN/SlaKoNet](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/scaling_analysis.ipynb)

<a name="basics"></a>
# Basics

A few preliminary notebooks before diving into materials design

1) [Introduction to high-performance computing](https://tacc-reproducible-intro-hpc.readthedocs.io/en/latest/intro_to_hpc/overview.html)
2) [Python beginners notebook](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/python_beginners_notebook.ipynb)
3) [Silicon atomic structure and analysis example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Simple_Silicon_atomic_structure_and_analysis_example.ipynb)

<a name="es"></a>
# Electronic structure

Density functional theory, Tight-binding and Beyond-DFT methods using various software

1) [Analyzing_data_in_the_JARVIS_DFT_dataset](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Analyzing_data_in_the_JARVIS_DFT_dataset.ipynb)
2) [Analyzing_data_in_the_JARVIS_Leaderboard](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Analyzing_data_in_the_JARVIS_Leaderboard.ipynb)
3) [Basic quantum espresso run](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_QuantumEspressoColab_Basic_Example.ipynb)
4) [JARVIS_Optoelectronics_Computational_screening_of_high_performance_optoelectronic_materials_using_OptB88vdW_and_TB_mBJ_formalisms ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_Optoelectronics_Computational_screening_of_high_performance_optoelectronic_materials_using_OptB88vdW_and_TB_mBJ_formalisms.ipynb)
5) [JARVIS_TopologicalSpillage_High_throughput_Discovery_of_Topologically_Non_trivial_Materials_using_Spin_orbit_Spillage ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_TopologicalSpillage_High_throughput_Discovery_of_Topologically_Non_trivial_Materials_using_Spin_orbit_Spillage.ipynb)
6) [JARVIS_Solar_Accelerated_Discovery_of_Efficient_Solar_Cell_Materials_Using_Quantum_and_Machine_Learning_Methods ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_Solar_Accelerated_Discovery_of_Efficient_Solar_Cell_Materials_Using_Quantum_and_Machine_Learning_Methods.ipynb)
7) [Si_bandstructure&densityof_states](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Si_bandstructure%26densityof_states.ipynb)
8) [JARVIS_Wannier90Example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_Wannier90Example.ipynb)
9) [BoltztrapExample](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/BoltztrapExample.ipynb)
10) [Making 2D heterostructures](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Making_2D_heterostructures.ipynb)
11)  [JARVIS_DFT_FormationEnergiesAccuracyCheck](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_DFT_FormationEnergiesAccuracyCheck.ipynb)
12) [Downloading raw analysis data and input/output files](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Download_raw_data_for_webpages.ipynb)
13) [JARVIS_DFT_2D_High_throughput_Identification_and_Characterization_of_Two_dimensional_Materials_using_Density_functional_theory ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_DFT_2D_High_throughput_Identification_and_Characterization_of_Two_dimensional_Materials_using_Density_functional_theory.ipynb)
14) [JARVIS_CONVERG_Convergence_and_machine_learning_predictions_of_Monkhorst_Pack_k_points_and_plane_wave_cut_off_in_high_throughput_DFT_calculations ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_CONVERG_Convergence_and_machine_learning_predictions_of_Monkhorst_Pack_k_points_and_plane_wave_cut_off_in_high_throughput_DFT_calculations.ipynb)
15) [JARVIS_DFPT_High_throughput_Density_Functional_Perturbation_Theory_and_Machine_Learning_Predictions_of_Infrared,_Piezoelectric_and_Dielectric_Responses ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_DFPT_High_throughput_Density_Functional_Perturbation_Theory_and_Machine_Learning_Predictions_of_Infrared%2C_Piezoelectric_and_Dielectric_Responses.ipynb)
16) [JARVIS_TE_Data_driven_discovery_of_3D_and_2D_thermoelectric_materials](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_TE_Data_driven_discovery_of_3D_and_2D_thermoelectric_materials.ipynb)
17) [JARVIS_ELAST_Elastic_properties_of_bulk_and_low_dimensional_materials_using_van_der_Waals_density_functional ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_ELAST_Elastic_properties_of_bulk_and_low_dimensional_materials_using_van_der_Waals_density_functional.ipynb)
18) [Get JARVIS-DFT final structures in ASE or Pymatgen format](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Get_JARVIS_DFT_final_structures_in_ASE_or_Pymatgen_format.ipynb)
19) [JARVIS_Solar_Accelerated_Discovery_of_Efficient_Solar_Cell_Materials_Using_Quantum_and_Machine_Learning_Methods ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_Solar_Accelerated_Discovery_of_Efficient_Solar_Cell_Materials_Using_Quantum_and_Machine_Learning_Methods.ipynb)
20) [JARVIS_TopologicalSpillage_High_throughput_Discovery_of_Topologically_Non_trivial_Materials_using_Spin_orbit_Spillage ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_TopologicalSpillage_High_throughput_Discovery_of_Topologically_Non_trivial_Materials_using_Spin_orbit_Spillage.ipynb)
21) [JARVIS_Optoelectronics_Computational_screening_of_high_performance_optoelectronic_materials_using_OptB88vdW_and_TB_mBJ_formalisms ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_Optoelectronics_Computational_screening_of_high_performance_optoelectronic_materials_using_OptB88vdW_and_TB_mBJ_formalisms.ipynb)
22) [JARVIS_QuantumEspressoColab_Designing_High_Tc_Superconductors_with_BCS_inspired_Screening,_Density_Functional_Theory_and_Deep_learning ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_QuantumEspressoColab_Designing_High_Tc_Superconductors_with_BCS_inspired_Screening%2C_Density_Functional_Theory_and_Deep_learning.ipynb)
22) [JARVIS_WTBH_Database_of_Wannier_tight_binding_Hamiltonians_using_high_throughput_density_functional_theory ](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_WTBH_Database_of_Wannier_tight_binding_Hamiltonians_using_high_throughput_density_functional_theory.ipynb)
23) [Comapre_MP_JV](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Comapre_MP_JV.ipynb)
24) [ParsingWebpages(JARVIS_DFT)](https://github.com/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ParsingWebpages(JARVIS_DFT).ipynb)
25) [ConvexHull](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ConvexHull.ipynb)
26) [DimensionalityAndExfoliationEnergy](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/DimensionalityAndExfoliationEnergy.ipynb)
27) [Element_filter_for_JARVIS_DFT_dataset](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Element_filter_for_JARVIS_DFT_dataset.ipynb)
28) [Run GPAW on Google-colab and calculate interface energy with jarvis-tools](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/GPAW_Colab.ipynb)
29) [ParsingWebpages(JARVIS_DFT)](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ParsingWebpages(JARVIS_DFT).ipynb)
30) [WTBH_MagneticMats.ipynb](https://github.com/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/WTBH_MagneticMats.ipynb)
31) [QMCPACK_Basic_Example](https://github.com/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/QMCPACK_Basic_Example.ipynb)

<a name="ff"></a>
# Force-field

1) [JARVIS_LAMMPS](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_LAMMPS.ipynb)
3) [MLFF SNAP training](https://colab.research.google.com/github/FitSNAP/FitSNAP/blob/master/tutorial.ipynb)
3) [ALIGNN-FF for energy and forces](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb)
4) [ALLEGRO training for Silicon](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Train_MLFF_ALLEGRO.ipynb)
5) [Analyzing_MOF_datasets](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Analyzing_MOF_datasets.ipynb)

<a name="ai"></a>
# Artificial intelligence/Machine learning

AI models for chemical formula, atomic structures, image and text for both forward and inverse design. Some of the methods include descriptor/feature based, graph based and transformers based designs.

1) [ML Sklearn Steel Fatigue](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ML_Sklearn.ipynb)  
2) [ML_Chem_Formula_Descriptors](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_ML_Magpie_AIMS.ipynb)
3) [Basic_Machine_learning_training_example_with_CFID_descriptors](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Simple_Machine_learning_training_example_with_CFID_descriptors.ipynb)
4) [JARVIS_ML_TrainingGPU](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_ML_TrainingGPU.ipynb)
5) [JARVIS_CFID_LightGBM_GPUvsCPU](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_ML_LightGBM_GPUvsCPU.ipynb)
6) [JARVIS_ML_TensorFlowExample](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_ML_TensorFlowExample.ipynb)
7) [JARVIS_Leaderboard_MatMiner](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/MatMiner_on_JARVIS_DFT.ipynb)
8) [CIF To Graph_example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/cif2graph.ipynb) 
9) [JARVIS_ALIGNN_Basic_Training_example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Training_ALIGNN_model_example.ipynb)
10) [ALIGNN-FF pretrained model/calculator usage](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ALIGNN_Structure_Relaxation_Phonons_Interface.ipynb)
11) [JARVIS_Leaderboard_contribution_ALIGNN](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/alignn_jarvis_leaderboard.ipynb)
12) [JARVIS_Leaderboard_MLFF/ALIGNN-FF for Silicon](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb)
13) [JARVIS_Leaderboard_KGCNN](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/kgcnn_jarvis_leaderboard.ipynb)
14) [ALIGNN Superconductor training](https://github.com/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ALIGNN_Sc.ipynb)
15) [Train ALLEGRO-FF for Silicon](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Train_MLFF_ALLEGRO.ipynb)
16) [Train NEQUIP-FF for Silicon](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Train_MLFF_NEQUIP.ipynb)
17) [Train CHGNet-FF for Silicon](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Train_CHGNet.ipynb)
18) [MatGL-FF_Mlearn for Silicon](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Matgl_mlearn.ipynb)
19) [SNAP-FF_Mlearn for Silicon](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/snap_mlearn.ipynb)
20) [Pretrained CHGNet Prediction](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/chgnet_prediction.ipynb)
21) [Pretrained OpenCatalystProject Model](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ocp_load_pretrained_models.ipynb)
22) [ALIGNN-Pretrained property-predictor models](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/GetTotalEnergy.ipynb)
23) [ALIGNN-PhononDos](https://colab.research.google.com/github/JARVIS-Materials-Design/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ALIGNN_PhononDOS.ipynb)
24) [Inverse design of superconductors with CDVAE](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/cdvae_example.ipynb)
25) [JARVIS_STEM_2D](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_STEM_2D.ipynb)
26) [AtomVision_Leaderboard_Example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/AtomVisionImageClassification.ipynb)
27) [AtomVision_Example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/AtomVisionExample.ipynb)
28) [ChemNLP example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ChemNLP_Example.ipynb)
29) [ChemNLP HuggingFace example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ChemNLP_TitleToAbstract.ipynb)
30) [AtomGPT training example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/atomgpt_example.ipynb)
31) [AtomGPT forward model training example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/atomgpt_forward_example.ipynb)
32) [AtomGPT inverse model training example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/atomgpt_example.ipynb)
33) [AtomGPT HuggingFace inference example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/atomgpt_example_huggingface.ipynb)
34) [Open catalyst project load model](https://github.com/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ocp_load_pretrained_models.ipynb)
35) [Vacancy formation ML](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/vacancy_ml.ipynb)
36) [Interface Materials Design/InterMat example](https://colab.research.google.com/gist/knc6/0d9aa89f671687c6e925eea2be9b824a/intermat_gettingstarted.ipynb)
37) [ALIGNN-FF Unified force-field structure relaxation/Phonons/Interface](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ALIGNN_Structure_Relaxation_Phonons_Interface.ipynb)
38) [ALIGNN-FF Melt Quench](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Fast_Melt_Quench.ipynb)
39) [Basic external tutorial on linear models](training_linear_models)
40) [Analyzing data in JARVIS-Leaderboard](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Analyzing_data_in_the_JARVIS_Leaderboard.ipynb)
41) [CHIPS-FF: Evaluation of universal machine learning force-fields](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/chipsff_example.ipynb)

<a name="qc"></a>
# Quantum computation

1) [With new qiskit package version: Quantum computation and Qiskit based electronic bandstructure](https://github.com/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Qiskit_based_electronic_bandstructure_latest_qiskit.ipynb)
2) [With old qiskit package version: Quantum computation and Qiskit based electronic bandstructure](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Qiskit_based_electronic_bandstructure_.ipynb)

<a name="exp"></a>
# Experiment related tutorials
1) [XRD Matching](https://github.com/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/XRD_Matching.ipynb)

# NanoHub FAIR workflow workshop/JARVIS-School @ Purdue university

[https://nanohub.org/FAIR_workshop_2024](https://nanohub.org/FAIR_workshop_2024)

1. Learn a basic DFT calculation
   
[Basic quantum espresso run](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_QuantumEspressoColab_Basic_Example.ipynb)

2. Once you run a lot of these, you can make a database, and analyze trends (AKA Exploratory Data Analysis)
   
[Analyzing_data_in_the_JARVIS_DFT_dataset](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Analyzing_data_in_the_JARVIS_DFT_dataset.ipynb)

3. These datasets can also be used to develop fast surrogate machine learning models
   
[JARVIS_Leaderboard_contribution_ALIGNN](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/alignn_jarvis_leaderboard.ipynb)

4. Beyond single property prediction models, they can be used to train machine-learning force-fields as well

[ALIGNN-FF for energy and forces](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb)

5. While the above MLFF was trained for single element system, a more generalized model was developed with JARVIS-DFT diverse dataset, and the developed model can be used for fast atomic structure optimization and phonon etc. property predictions

[ALIGNN-FF Unified force-field structure relaxation](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb)

6. While the above ML models were for forward design, we can use AtomGPT for inverse design as well

[AtomGPT training example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/atomgpt_example.ipynb)

[AtomGPT HuggingFace inference example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/atomgpt_example_huggingface.ipynb)
    
7. Other optional notebooks for the tutorial session

[AtomVision_Example](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/AtomVisionExample.ipynb)

[JARVIS_LAMMPS](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/JARVIS_LAMMPS.ipynb)

[Quantum computation and Qiskit based electronic bandstructure](https://github.com/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Qiskit_based_electronic_bandstructure_latest_qiskit.ipynb)
   
# AIMS2024 tutorial and presentation slides

[https://github.com/usnistgov/aims2024_workshop](https://github.com/usnistgov/aims2024_workshop)

<a name="school"></a>
# JARVIS-School

[More info](https://jarvis.nist.gov/events/school)

<a name="aps"></a>
# APS2023 tutorial

[More info](https://march.aps.org/events/tutorial-graph-neural-networks)

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# AIMS2022 tutorial

[More info](https://jarvis.nist.gov/events/aims)

<a name="refs"></a>
References
-----------------

[The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design](https://www.nature.com/articles/s41524-020-00440-1) & [various other publications](https://jarvis-tools.readthedocs.io/en/master/publications.html)

<a name="contrib"></a>
How to contribute
-----------------

For detailed instructions, please see [Contribution instructions](https://github.com/usnistgov/jarvis/blob/master/Contribution.rst)

<a name="corres"></a>
Correspondence
--------------------

Please report bugs as [Github issues](https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks/issues)(preferred) or email to kamal.choudhary@nist.gov.

<a name="fund"></a>
Funding support
--------------------

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

Code of conduct
--------------------

Please see [Code of conduct](https://github.com/usnistgov/jarvis/blob/master/CODE_OF_CONDUCT.md)

<a name="license"></a>
License
--------------------
[NIST License](https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst)

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