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Table of Contents

JARVIS Leaderboard (Introduction)

This project provides benchmark-performances of various methods for materials science applications using the datasets available in JARVIS-Tools databases. Some of the categories are: Artificial Intelligence (AI), Electronic Structure (ES), Force-field (FF), Quantum Computation (QC) and Experiments (EXP). There are a variety of properties included in the benchmark. In addition to prediction results, we attempt to capture the underlyig software, hardware and instrumental frameworks to enhance reproducibility. This project is a part of the NIST-JARVIS infrastructure.

  • Number of methods: 150
  • Number of benchmarks: 269
  • Number of contributions: 1266
  • Number of datapoints: 8713782

How to use

Click below to expand

How to use this website?

Check the tabs below:

For a short version, checkout this google colab-notebook

For a long version, see below:

  1. Fork the jarvis_leaderboard repository
  2. git clone https://github.com/USERNAME/jarvis_leaderboard, use your own GitHub USERNAME, e.g. knc6, instead of usnistgov

    Note if you do not use forked version, you won't be able to make a pull request

  3. cd jarvis_leaderboard

  4. conda create --name leaderboard python=3.8
  5. source activate leaderboard
  6. Install the package: python setup.py develop
  7. Let's add a contribution for Silicon bandgap using DFT PBE (an Electronic structure approach)

cd jarvis_leaderboard/contributions/

mkdir vasp_pbe_teamX , you can give any reaosnable name to the benchmark folder in place of vasp_pbe_teamX

cd vasp_pbe_teamX

cp ../vasp_optb88vdw/ES-SinglePropertyPrediction-bandgap_JVASP_1002_Si-dft_3d-test-mae.csv.zip .

vi ES-SinglePropertyPrediction-bandgap_JVASP_1002_Si-dft_3d-test-mae.csv.zip

Note: do not change filenames, e.g., replace dft with qmc etc., which will cause errors

After pressing eneter twice, you'll see the file content as id,prediction

Just modify the predicting value to your model/measurement value

Save the file (":wq!" and ":q!")

Add metadata.json and run.sh files to capture metadata and enhance reproducibility. The metadata file must have at least your project_url and model_name info. The project_url couls be a publication/GitHub page etc.

Note: An admin will run your run.sh to check if he/she can reproduce your benchmark

Now, cd ../../../

python jarvis_leaderboard/rebuild.py

which will compile all data, compare with reference dataset and calculate metrices

Hoping there's no error, try: mkdocs serve

Ensure vasp_pbe_teamX row exists at:

http://127.0.0.1:8000/usnistgov/jarvis_leaderboard/ES/SinglePropertyPrediction/bandgap_JVASP_1002_Si/

Now add changes, git add jarvis_leaderboard/contributions/vasp_pbe_teamX

Commit your changes, git commmit -m 'Adding my PBE Si result.'

git push

Now go to your forked github repo and make a pull reuqest (PR) to usnistgov/jarvis_leaderboard in develop branch

If you are not familiar with pull requests checkout this link

Note: only admins are allowed to make pull requests to main branch

Once an admin approve the PR, you'll see your results on the official leaderboard

  1. Another example for AI mode as follows:

Populate the dataset for a benchmark, e.g.:

python jarvis_leaderboard/populate_data.py --benchmark_file AI-SinglePropertyPrediction-exfoliation_energy-dft_3d-test-mae --output_path=Out

Currently, this script works for atomistic tasks only, addition of other tasks will be available soon.

Train you model(s), e.g.:

pip install alignn

wget https://raw.githubusercontent.com/usnistgov/alignn/main/alignn/examples/sample_data/config_example.json

train_folder.py --root_dir "Out" --config "config_example.json" --output_dir="temp"

Create a folder in the jarvis_leaderboard/contributions folder under respective submodule, e.g.:

mkdir contributions/my_awesome_model

Add comma-separated zip file (.csv.zip) file(s) corresponding to benchmark(s), e.g.:

cp temp/prediction_results_test_set.csv .

mv prediction_results_test_set.csv AI-SinglePropertyPrediction-exfoliation_energy-dft_3d-test-mae.csv

zip AI-SinglePropertyPrediction-exfoliation_energy-dft_3d-test-mae.csv AI-SinglePropertyPrediction-exfoliation_energy-dft_3d-test-mae.csv.zip

mv AI-SinglePropertyPrediction-exfoliation_energy-dft_3d-test-mae.csv.zip jarvis_leaderboard/contributions/my_awesome_model

Add metadata info in the metadata.json file, e.g.:

cp jarvis_leaderboard/contributions/alignn_models/metadata.json jarvis_leaderboard/contributions/my_awesome_model

Also, add a run.py, run.sh and Dockerfile scripts to reproduce the model predictions.

Run python jarvis_leaderboard/rebuild.py to check there are no errors

Run mkdocs serve to check if the new benchmark exists, e.g. at page http://127.0.0.1:8000/usnistgov/jarvis_leaderboard/AI/SinglePropertyPrediction/exfoliation_energy/

Add. commit and push your changes, e.g.:

git add jarvis_leaderboard/contributions/my_awesome_model

git commit -m 'Adding my awesome_model to jarvis_leaderboard

git push origin main

Make a pull request from your fork to the source repo at usnistgov/jarvis_leaderboard develop branch

Notes:

  1. The word: SinglePropertyPrediction: task type, test, property: exfoliation_energy, dataset: dft_3d, method: AI, metric: mae have been joined with '-' sign. This format should be used for consistency in webpage generation.

  2. The test data splits are pre-determined, if the exact test IDs are not used, then the code might result in errors.

  1. Create a json.zip file in the jarvis_leaderboard/benchmarks folder under respective sub-category, e.g.:

    e.g. jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_exfoliation_energy.json.zip.

  2. In the .json file should have train, val, test keys with array of ids and their values.

    Note train and 'val` can be empty dictionaries if the benchmarks are other than AI method

  3. Add a .md file, e.g.: jarvis_leaderboard/docs/AI/SinglePropertyPrediction/exfoliation_energy.md. This is where contributions performers will be kept and website info will be generated.

  4. An example for creating such a file is provided in: jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/transform_from_figshare.py

  5. Then follow the instructions for "Adding model benchmarks to existing dataset"

Notes: A new benchmark must be linked with a peer-reviewed article and must have a DOI to ensure a minimum quality assurance for the data. We recommend adding your large dataset in Figshare or similar repository and then integrate it in JARVIS-Tools We also recommend to use JARVIS-Tools for generating dataset/models/benchmarks which can help us maintain the benchmark for long term.
Methods used for generating the data and referece are given below:

Method used for results Methods for comparison
EXP EXP/ES/analytical results
ES ES/EXP
FF ES/EXP
QC Classical/analytical results
AI Test set data

  1. MAE: Mean Absolute Error
  2. ACC: Classification accuracy
  3. MULTIMAE: MAE sum of multple entries, Euclidean distance
  4. For names of datasets and associated propertiesm refer to datasets in JARVIS-Tools

Ask a question/raise an issue on GitHub. You can also email Kamal Choudhary if needed (kamal.choudhary@nist.gov). However, we recommend using the GitHub issues for any questions/concerns.

@article{choudhary2020joint, title={The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design}, author={Choudhary, Kamal and Garrity, Kevin F and Reid, Andrew CE and DeCost, Brian and Biacchi, Adam J and Hight Walker, Angela R and Trautt, Zachary and Hattrick-Simpers, Jason and Kusne, A Gilad and Centrone, Andrea and others}, journal={npj computational materials}, volume={6}, number={1}, pages={173}, year={2020}, publisher={Nature Publishing Group UK London} }

Summary table

Category/Sub-cat.SinglePropertyPredictionSinglePropertyClassMLFFTextClassTokenClassTextSummaryTextGenImageClassSpectraEigenSolver
AI30721571811121-
ES775-------10-
FF47---------
QC---------6
EXP1-------18-

Quick start using GoogleColab notebook examples

  1. Analyzing_data_in_the_JARVIS_Leaderboard.ipynb
  2. Upload_benchmark_to_jarvis_leaderboard.ipynb
  3. alignn_jarvis_leaderboard.ipynb
  4. kgcnn_jarvis_leaderboard.ipynb
  5. MatMiner_on_JARVIS_DFT.ipynb
  6. AtomVision_Image_Classification.ipynb

Terminologies used in this project

  • Categories: are of following types Artificial Intelligence (AI), Electronic Structure (ES), Force-field (FF), Quantum Computation (QC) and Experiments (EXP). Each of these categories are divided into sub-categories. These sub-categories include single-property-prediction, single-property-classification, machine-learning force-fields, text-classification, text-token classification, text-generation, image classification, image-segmentation, image-generation, spectra-prediction, and eigensolver. These categories and sub-categories are highly flexible and new entries can be easily added.

  • Sub-categories: include 1) SinglePropertyPrediction (where the output of a model/experiment is one single number for an entry), 2) SinglePropertyClass (where the output is class-ids, e.g., 0,1,.. instead of floating values), 3) ImageClass (for multi-class image classification), 4) textClass (for multi-label text classification), 5) MLFF (machine learning force-field), 6) Spectra (for multi-value data) and 7) EigenSolver (for Hamiltonian simulation).

  • Benchmarks: are ground truth data used to calculate metrics for each specific task (e.g a json.zip file).

  • Methods: are a set of precise specifications for evaluation against a benchmark. For example, within ES category, DFT with VASP-GGA-PAW-PBE are specifications, hence a method. Similarly, within AI category, descriptor/feature based models with MatMiner-chemical features and LightGBM software are specifications, hence a method.

  • Contributions: are individual data in form of csv.zip files for each benchmark and specific method. Each contribution has six components: method (e.g. AI), category (e.g. SinglePropertyPrediction), property (e.g. formation energy), dataset (e.g. dft_3d), data-split (e.g. test), metric (e.g. mae).

List of benchmarks

Click on the entries in the Benchmark column. You'll be able to see methods available for each benchmark, CSV file submitted for the contribution, JSON file for ground trutch data, run.sh script for running the method and Info for metadata associated with the method.

CategorySub-categoryBenchmarkMethodMetricScoreTeamDatasetSize
AIImageClassstem_2d_image_bravais_classdensenet_modelACC0.83JARVISstem_2d_image9150
AIMLFFalignn_ff_db_energyalignnff_wt0.1MAE0.034JARVISalignn_ff_db307111
AIMLFFmlearn_Cu_energyalignnff_wt1_mlearn_onlyMAE0.792JARVISmlearn_Cu293
AIMLFFmlearn_Ge_energyalignnff_wt1_mlearn_onlyMAE1.427JARVISmlearn_Ge253
AIMLFFmlearn_Li_energyalignnff_wt1_mlearn_onlyMAE0.56JARVISmlearn_Li270
AIMLFFmlearn_Mo_energyalignnff_wt1_mlearn_onlyMAE1.408JARVISmlearn_Mo217
AIMLFFmlearn_Ni_energyalignnff_wt1_mlearn_onlyMAE2.463JARVISmlearn_Ni294
AIMLFFmlearn_Si_energyalignnff_wt1_mlearn_onlyMAE1.079JARVISmlearn_Si239
AIMLFFalignn_ff_db_forcesm3gnet_pretrainedMULTIMAE0.089M3GNETalignn_ff_db307111
AIMLFFmlearn_Cu_forcesm3gnet_pretrainedMULTIMAE0.065M3GNETmlearn_Cu293
AIMLFFmlearn_Ge_forcesalignnff_wt1_mlearn_onlyMULTIMAE0.084JARVISmlearn_Ge253
AIMLFFmlearn_Li_forcesalignnff_wt1_mlearn_onlyMULTIMAE0.028JARVISmlearn_Li270
AIMLFFmlearn_Mo_forcesalignnff_wt1_mlearn_onlyMULTIMAE0.155JARVISmlearn_Mo217
AIMLFFmlearn_Ni_forceschgnet_pretrainedMULTIMAE0.066CHGNETmlearn_Ni294
AIMLFFmlearn_Si_forcesalignnff_wt1_mlearn_onlyMULTIMAE0.115JARVISmlearn_Si239
AISinglePropertyClassdft_3d_magmom_oszicarmatminer_xgboostACC0.949UofTdft_3d52210
AISinglePropertyClassdft_3d_mbj_bandgapmatminer_xgboostACC0.94UofTdft_3d18167
AISinglePropertyClassdft_3d_n_powerfactmatminer_xgboostACC0.819UofTdft_3d23210
AISinglePropertyClassdft_3d_optb88vdw_bandgapmatminer_xgboostACC0.936UofTdft_3d55713
AISinglePropertyClassdft_3d_p_Seebeckmatminer_xgboostACC0.933UofTdft_3d23210
AISinglePropertyClassdft_3d_slmematminer_rfACC0.842UofTdft_3d9062
AISinglePropertyClassdft_3d_spillagematminer_xgboostACC0.836UofTdft_3d11375
AISinglePropertyPredictionsnumat_Band_gap_HSEalignn_modelMAE0.377ALIGNNsnumat10386
AISinglePropertyPredictionqm9_std_jctc_Cvalignn_modelMAE0.024ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionqm9_std_jctc_Galignn_modelMAE0.013ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionqm9_std_jctc_Halignn_modelMAE0.014ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionqm9_std_jctc_HOMOalignn_modelMAE0.019ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionhalide_peroskites_HSE_decomp_energyalignn_modelMAE0.051ALIGNNhalide_peroskites229
AISinglePropertyPredictionhalide_peroskites_HSE_gapalignn_modelMAE0.161ALIGNNhalide_peroskites229
AISinglePropertyPredictionqm9_std_jctc_LUMOalignn_modelMAE0.018ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionhalide_peroskites_PBE_decomp_energyalignn_modelMAE0.048ALIGNNhalide_peroskites229
AISinglePropertyPredictionhalide_peroskites_PBE_gapalignn_modelMAE0.122ALIGNNhalide_peroskites229
AISinglePropertyPredictionqm9_std_jctc_R2alignn_modelMAE0.652ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionhalide_peroskites_Ref_indalignn_modelMAE0.023ALIGNNhalide_peroskites229
AISinglePropertyPredictionsupercon_chem_Tcmatminer_rfMAE4.851UofTsupercon_chem16414
AISinglePropertyPredictionqm9_std_jctc_Ualignn_modelMAE0.014ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionqm9_std_jctc_U0alignn_modelMAE0.015ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionqm9_std_jctc_ZPVEalignn_modelMAE0.002ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionqm9_std_jctc_alphaalignn_modelMAE0.056ALIGNNqm9_std_jctc130829
AISinglePropertyPredictiondft_3d_avg_elec_massalignn_modelMAE0.085ALIGNNdft_3d17642
AISinglePropertyPredictiondft_3d_avg_hole_massalignn_modelMAE0.124ALIGNNdft_3d17642
AISinglePropertyPredictionqmof_bandgapalignn_modelMAE0.202ALIGNNqmof20424
AISinglePropertyPredictiondft_3d_bulk_modulus_kvalignn_modelMAE10.399ALIGNNdft_3d19680
AISinglePropertyPredictiondft_3d_dfpt_piezo_max_dielectricalignn_modelMAE28.151ALIGNNdft_3d4704
AISinglePropertyPredictiondft_3d_dfpt_piezo_max_dijcgcnn_modelMAE16.013CGCNNdft_3d3345
AISinglePropertyPredictionmegnet_e_formalignn_modelMAE0.022ALIGNNmegnet69239
AISinglePropertyPredictiontinnet_N_eadalignn_modelMAE0.087ALIGNNtinnet_N327
AISinglePropertyPredictionvacancydb_efalignnff_wt0.1MAE0.939JARVISvacancydb431
AISinglePropertyPredictionvacancydb_2D_efalignnff_wt0.1MAE1.034JARVISvacancydb_2D70
AISinglePropertyPredictionvacancydb_elements_efalignn_pretrained_energy_modelMAE1.26JARVISvacancydb_elements78
AISinglePropertyPredictionvacancydb_oxides_efalignnff_wt0.1MAE0.659JARVISvacancydb_oxides30
AISinglePropertyPredictiondft_3d_ehullpotnetMAE0.052DIVE@TAMUdft_3d55364
AISinglePropertyPredictiondft_3d_encutalignn_modelMAE133.796ALIGNNdft_3d55386
AISinglePropertyPredictionqe_tb_energy_per_atomalignn_modelMAE0.751ALIGNNqe_tb829574
AISinglePropertyPredictiondft_3d_epsxalignn_modelMAE20.394ALIGNNdft_3d44490
AISinglePropertyPredictiondft_3d_epsyalignn_modelMAE19.999ALIGNNdft_3d44490
AISinglePropertyPredictiondft_3d_epszalignn_modelMAE19.568ALIGNNdft_3d44490
AISinglePropertyPredictiondft_3d_exfoliation_energymatminer_xgboostMAE40.887UofTdft_3d812
AISinglePropertyPredictionqe_tb_f_enpalignn_modelMAE0.102ALIGNNqe_tb829574
AISinglePropertyPredictionqe_tb_final_energymatminer_rfMAE1.471UofTqe_tb829574
AISinglePropertyPredictiondft_3d_formation_energy_peratompotnetMAE0.029DIVE@TAMUdft_3d55713
AISinglePropertyPredictionssub_formula_energyElemNet2_TLMAE0.092NorthWestern_Universityssub1726
AISinglePropertyPredictionqm9_std_jctc_gapalignn_modelMAE0.035ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionmegnet_gappbealignn_modelMAE0.218ALIGNNmegnet69239
AISinglePropertyPredictionqe_tb_indir_gapmatminer_rfMAE0.028UofTqe_tb829574
AISinglePropertyPredictiondft_3d_kpoint_length_unitmatminer_xgboostMAE9.047UofTdft_3d55392
AISinglePropertyPredictionhmof_lcdmatminer_xgboostMAE0.459UofThmof137651
AISinglePropertyPredictiondft_3d_magmom_oszicaralignn_modelMAE0.257ALIGNNdft_3d52210
AISinglePropertyPredictionmag2d_chem_magnetic_momentElemNet2_TLMAE0.3NorthWestern_Universitymag2d_chem226
AISinglePropertyPredictionhmof_max_co2_adspmatminer_xgboostMAE0.462UofThmof137651
AISinglePropertyPredictiondft_3d_max_efgalignn_modelMAE19.121ALIGNNdft_3d11865
AISinglePropertyPredictiondft_3d_mbj_bandgappotnetMAE0.271DIVE@TAMUdft_3d18167
AISinglePropertyPredictiondft_3d_mepsxalignn_modelMAE24.046ALIGNNdft_3d16809
AISinglePropertyPredictiondft_3d_mepsyalignn_modelMAE23.648ALIGNNdft_3d16809
AISinglePropertyPredictiondft_3d_mepszalignn_modelMAE23.731ALIGNNdft_3d16809
AISinglePropertyPredictionhmof_min_co2_adspalignn_modelMAE0.038ALIGNNhmof137651
AISinglePropertyPredictionqm9_std_jctc_mualignn_modelMAE0.025ALIGNNqm9_std_jctc130829
AISinglePropertyPredictiondft_3d_n_Seebeckalignn_modelMAE40.921ALIGNNdft_3d23210
AISinglePropertyPredictiondft_3d_n_powerfactalignn_modelMAE442.299ALIGNNdft_3d23210
AISinglePropertyPredictiondft_3d_optb88vdw_bandgappotnetMAE0.125DIVE@TAMUdft_3d55713
AISinglePropertyPredictiondft_3d_optb88vdw_total_energypotnetMAE0.032DIVE@TAMUdft_3d55713
AISinglePropertyPredictiondft_3d_ph_heat_capacitymatminer_rfMAE5.276UofTdft_3d12054
AISinglePropertyPredictionhmof_pldmatminer_xgboostMAE0.573UofThmof137651
AISinglePropertyPredictionocp100k_relaxed_energyalignn_modelMAE0.629ALIGNNocp100k149886
AISinglePropertyPredictionocp10k_relaxed_energyalignn_modelMAE0.762ALIGNNocp10k59886
AISinglePropertyPredictionocp_all_relaxed_energyalignn_modelMAE0.599ALIGNNocp_all510214
AISinglePropertyPredictiondft_3d_shear_modulus_gvalignn_modelMAE9.476ALIGNNdft_3d19680
AISinglePropertyPredictiondft_3d_slmealignn_modelMAE4.521ALIGNNdft_3d9062
AISinglePropertyPredictiondft_3d_spillagealignn_modelMAE0.351ALIGNNdft_3d11375
AISinglePropertyPredictionhmof_surface_area_m2cm3matminer_xgboostMAE83.358UofThmof137651
AISinglePropertyPredictionhmof_surface_area_m2galignn_modelMAE91.15ALIGNNhmof137651
AISinglePropertyPredictionhmof_void_fractionalignn_modelMAE0.017ALIGNNhmof137651
AISpectraedos_pdos_ph_dosalignn_modelMULTIMAE0.058ALIGNNedos_pdos14243
AITextClassarXiv_categoriessvc_model_text_abstractACC0.908ChemNLParXiv100994
AITextClasspubchem_categoriesrandom_forest_text_title_abstractACC0.967ChemNLPpubchem44500
AITextGenarxiv_gen_texttransformers_gpt2_mediumROUGE0.317ChemNLParxiv_gen490
AITextSummaryarxiv_summary_texttransformers_t5_baseROUGE0.39ChemNLParxiv_summary87148
AITokenClassmat_scholar_ner_labelstransformers_xlnetACC0.748ChemNLPmat_scholar_ner123267
ESSinglePropertyPredictiondft_3d_Tc_superconqe_pbesol_gbrvMAE3.378JARVISdft_3d14
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_1014_Taqe_pbesol_gbrvMAE3.14JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_1151_MgB2qe_pbesol_gbrvMAE6.315JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_11981_Nb3Alqe_pbesol_gbrvMAE7.844JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_14492_NbOqe_pbesol_gbrvMAE2.213JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_14837_Vqe_pbesol_gbrvMAE12.957JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_14960_V3Siqe_pbesol_gbrvMAE0.633JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_15938_Nb3Siqe_pbesol_gbrvMAE1.499JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_19679_ZrNqe_pbesol_gbrvMAE0.264JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_19889_NbCqe_pbesol_gbrv_tetrahedronMAE1.0JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_20620_YB6qe_pbesol_gbrvMAE2.06JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_36335_NbNqe_pbesol_gbrvMAE1.565JARVISdft_3d1
ESSinglePropertyPredictiondft_2d_Tc_supercon_JVASP_646_NbS2qe_pbesol_gbrvMAE2.2JARVISdft_2d1
ESSinglePropertyPredictiondft_2d_Tc_supercon_JVASP_655_NbSe2qe_pbesol_gbrvMAE1.9JARVISdft_2d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_816_Alqe_pbesol_gbrvMAE0.399JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_934_Nbqe_pbesol_gbrvMAE1.423JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_961_Pbqe_pbesol_gbrv_tetrahedronMAE1.8JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgapvasp_tbmbjMAE0.498JARVISdft_3d54
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1002_Sigpaw_gllbscMAE0.005GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_104_TiO2tb3_modelsMAE0.311TB3dft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1130_LiFgpaw_gllbscMAE0.77GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_113_ZrO2vasp_tbmbjMAE1.287JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1145_KClvasp_tbmbjMAE0.091JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_116_MgOgpaw_gllbscMAE0.665GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1174_GaAsvasp_tbmbjMAE0.199JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1180_InNvasp_tbmbjMAE0.038JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1183_InPvasp_tbmbjMAE0.033JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1189_InSbgpaw_ldaMAE0.024GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1192_CdSevasp_tbmbjMAE0.01JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1198_ZnTevasp_tbmbjMAE0.157JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1201_CuClgpaw_gllbscMAE0.708GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1216_Cu2Ogpaw_gllbscMAE0.956GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1267_BaTegpaw_gllbscMAE0.253GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1294_BaSegpaw_gllbscMAE0.567GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1300_MgSvasp_tbmbjMAE0.504JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1312_BPvasp_tbmbjMAE0.186JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1315_BaSvasp_tbmbjMAE0.601JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1327_AlPvasp_tbmbjMAE0.063JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1393_GaPvasp_tbmbjMAE0.023JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1405_CaOgpaw_gllbscMAE0.438GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1408_AlSbvasp_tbmbjMAE0.087JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1453_AlCuO2gpaw_gllbscMAE0.259GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1702_ZnSgpaw_gllbscMAE0.056GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_17_BNvasp_tbmbjMAE0.085JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1954_AgClvasp_tbmbjMAE0.372JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_23_CdTevasp_tbmbjMAE0.03JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_299_SnSevasp_optb88vdwMAE0.192JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_30_GaNgpaw_gllbscMAE0.338GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_32_Al2O3gpaw_gllbscMAE0.488GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_39_AlNgpaw_gllbscMAE0.012GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_54_MoS2vasp_tbmbjMAE0.047JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_57_MoSe2gpaw_pbeMAE0.034GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_5_TiO2tb3_modelsMAE0.201TB3dft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_72_WS2tb3_modelsMAE0.067TB3dft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_75_WSe2gpaw_pbeMAE0.01GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_7630_BAsvasp_optb88vdwMAE0.045JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_7678_MgSevasp_optb88vdwMAE0.353JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_7762_MgTevasp_tbmbjMAE0.106JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_7844_AlNvasp_tbmbjMAE0.099JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_7860_SnTetb3_modelsMAE0.039TB3dft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_8003_CdSvasp_tbmbjMAE0.02JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_8082_SrTiO3tb3_modelsMAE0.627TB3dft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_8158_SiCvasp_tbmbjMAE0.112JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_8169_GaNgpaw_gllbscMAE0.198GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_8566_AgIvasp_tbmbjMAE0.821JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_8583_AgBrvasp_tbmbjMAE0.194JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_890_Geqe_pbe_ccECPMAE0.049JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_9147_HfO2vasp_tbmbjMAE0.036JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_91_Cgpaw_gllbscMAE0.228GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_95_CdSvasp_tbmbjMAE0.101JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_96_ZnSevasp_tbmbjMAE0.19JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_97_InAsvasp_tbmbjMAE0.022JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulusvasp_opt86bMAE4.662JARVISdft_3d21
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_1002_Sivasp_scanMAE0.669JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_1130_LiFvasp_pbe_mpMAE0.2MPdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_113_ZrO2qmcpack_team__dmc_003MAE42.0QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_116_MgOvasp_optb88vdwMAE4.33JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_1174_GaAsqmcpack_dmc_pbeMAE0.967JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_1306_BaOqmcpack_team__dmc_002MAE2.35QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_1312_BPqmcpack_team__dmc_001MAE4.85QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_1390_SrOqmcpack_team__dmc_002MAE1.69QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_1405_CaOqmcpack_team__dmc_002MAE0.38QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_14590_Hfqmcpack_team__dmc_003MAE0.4QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_14604_Bavasp_optcx13MAE0.1JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_14606_Agvasp_opt86bMAE1.6JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_14612_Zrqmcpack_team__dmc_003MAE4.0QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_14813_Rbvasp_pbe_mpMAE0.1MPdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_182_SiCvasp_optcx13MAE7.1JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_20290_Sc2O3qmcpack_team__dmc_002MAE29.65QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_20326_NaFvasp_opt86bMAE0.5JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_20793_Y2O3qmcpack_team__dmc_002MAE23.7QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_21208_Srvasp_opt86bMAE0.0JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_23862_NaClvasp_opt86bMAE0.3JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_23864_LiClqmcpack_team__dmc_001MAE0.13QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_25065_Livasp_optcx13MAE0.0JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_25114_Kvasp_opt86bMAE0.1JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_25180_Cavasp_opt86bMAE0.5JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_25213_Krqmcpack_team__dmc_001MAE0.24QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_25248_Xeqmcpack_team__dmc_001MAE0.19QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_34249_HfO2qmcpack_team__dmc_003MAE55.0QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_350_ZrO2qmcpack_team__dmc_003MAE93.0QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_7809_ZrO2qmcpack_team__dmc_003MAE30.0QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_7870_MnOqmcpack_team__dmc_002MAE10.0QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_7871_NiOqmcpack_team__dmc_002MAE5.0QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_79204_BNqmcpack_team__dmc_001MAE10.86QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_816_Alvasp_optcx13MAE0.2JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_819_Arqmcpack_team__dmc_001MAE0.42QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_834_Beqmcpack_team__dmc_001MAE2.37QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_867_Cuvasp_optb88vdwMAE0.6JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_87128_CoOqmcpack_team__dmc_002MAE1.0QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_890_Geqmcpack_dmc_pbeMAE2.184JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_9147_HfO2qmcpack_team__dmc_003MAE8.0QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_91_Cvasp_optcx13MAE4.0JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_92796_La2O3qmcpack_team__dmc_002MAE20.34QMCPACKdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_963_Pdvasp_optcx13MAE5.1JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_984_Rhvasp_optb88vdwMAE8.3JARVISdft_3d1
ESSinglePropertyPredictionvacancydb_efvasp_optb88vdwMAE0.305JARVISvacancydb38
ESSinglePropertyPredictiondft_3d_epsxvasp_optb88vdw_linoptMAE1.464JARVISdft_3d16
ESSinglePropertyPredictiondft_3d_epsx_JVASP_1312_BPvasp_optb88vdw_linoptMAE1.893JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_1327_AlPvasp_optb88vdw_linoptMAE1.073JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_1393_GaPvasp_optb88vdw_linoptMAE0.489JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_1408_AlSbvasp_optb88vdw_linoptMAE0.337JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_1702_ZnSvasp_optb88vdw_dfptMAE1.391JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_17_BNvasp_optb88vdw_linoptMAE0.307JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_182_SiCvasp_optb88vdw_linoptMAE0.384JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_2355_ZnGeP2vasp_optb88vdw_dfptMAE0.286JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_2376_ZnSiP2vasp_optb88vdw_linoptMAE0.171JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_23_CdTevasp_optb88vdw_linoptMAE2.923JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_54_MoS2vasp_optb88vdw_linoptMAE0.865JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_57_MoSe2vasp_optb88vdw_linoptMAE0.505JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_60_Te2Movasp_optb88vdw_linoptMAE0.727JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_72_WS2vasp_optb88vdw_dfptMAE2.41JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_75_WSe2vasp_optb88vdw_dfptMAE3.724JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_epsx_JVASP_8041_HgTevasp_optb88vdw_linoptMAE3.225JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelecvasp_optb88vdw_dfptMAE1.085JARVISdft_3d16
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_110_BaTiO3vasp_optb88vdw_dfptMAE5.109JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_1180_InNvasp_optb88vdw_dfptMAE0.009JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_1195_ZnOvasp_optb88vdw_dfptMAE0.763JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_1240_LiNbO3vasp_optb88vdw_dfptMAE0.707JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_1327_AlPvasp_optb88vdw_dfptMAE0.765JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_1372_AlAsvasp_optb88vdw_dfptMAE0.82JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_1408_AlSbvasp_optb88vdw_dfptMAE0.671JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_20778_BeOvasp_optb88vdw_dfptMAE0.487JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_30_GaNvasp_optb88vdw_dfptMAE0.313JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_3450_TiPbO3vasp_optb88vdw_dfptMAE4.376JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_35711_GaSbvasp_optb88vdw_dfptMAE0.84JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_39_AlNvasp_optb88vdw_dfptMAE1.131JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_41_SiO2vasp_optb88vdw_dfptMAE0.012JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_57695_BNvasp_optb88vdw_dfptMAE0.497JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_7648_ZnSvasp_optb88vdw_dfptMAE0.397JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_max_piezoelec_JVASP_8047_ZnSevasp_optb88vdw_dfptMAE0.469JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_slmevasp_tbmbjMAE5.093JARVISdft_3d5
ESSinglePropertyPredictiondft_3d_slme_JVASP_1174_GaAsvasp_tbmbjMAE3.94JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_slme_JVASP_266_InPvasp_tbmbjMAE9.14JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_slme_JVASP_7112_H6PbCI3Nvasp_tbmbjMAE0.52JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_slme_JVASP_7757_CdTevasp_tbmbjMAE6.77JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_slme_JVASP_8554_InCuSe2vasp_tbmbjMAE8.35JARVISdft_3d1
ESSpectradft_3d_dielectric_functionvasp_tbmbjMULTIMAE2.88JARVISdft_3d4
ESSpectradft_3d_dielectric_function_JVASP_1002_Sivasp_tbmbjMULTIMAE3.392JARVISdft_3d1
ESSpectradft_3d_dielectric_function_JVASP_1174_GaAsvasp_tbmbjMULTIMAE2.672JARVISdft_3d1
ESSpectradft_3d_dielectric_function_JVASP_266_InPvasp_tbmbjMULTIMAE2.531JARVISdft_3d1
ESSpectradft_3d_dielectric_function_JVASP_890_Gevasp_tbmbjMULTIMAE2.925JARVISdft_3d1
EXPSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_1151_MgB2ppms_magnetizationMAE0.22PML-PPMSdft_3d1
EXPSpectradft_3d_XRD_JVASP_19821_MgB2bruker_d8MULTIMAE0.02MML-BrukerD8dft_3d1
EXPSpectranist_isodb_co2_RM_885210.1007s10450-018-9958-x.Lab01MULTIMAE0.021FACTlabnist_isodb1
EXPSpectramidas_stress_strain_vibroscopy_kevlar129favimat_5MULTIMAE0.014FAVIMATmidas1
FFSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_1002_SiSi.tersoffMAE1.427JARVIS-FFdft_3d1
FFSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_816_AlNiAlH_jea.eam.alloyMAE0.027JARVIS-FFdft_3d1
FFSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_867_CuMendelev_Cu2_2012.eam.fsMAE0.837JARVIS-FFdft_3d1
FFSinglePropertyPredictionlj_2d_liquid_viscositylammps_2d_liquid_einsteinMAE0.024LAMMPSlj_2d_liquid1
QCEigenSolverdft_3d_electron_bands_JVASP_816_Al_WTBHqiskit_vqd_SU2_c6MULTIMAE0.003JARVISdft_3d1

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