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Explore State-of-the-Art Materials Design Methods

Artificial intelligence (AI)

Contributions: 617

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Electronic Struct. (ES)

Contributions: 742

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Force-field (FF)/potentials

Contributions 282

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Quantum Comput. (QC)

Contributions: 6

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Experiments (EXP)

Contributions: 25

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Example Notebooks

Notebooks: 16

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Methodologies

Available Methods:395

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Contribution Guide

Contributors: 26

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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.

Usually, codes are kept in GitHub/GitLab etc., data is kept in Zenodo/Figshare/NIST Materials data etc., we recommend keeping the benchmarks in the JARVIS-Leaderboard to enhance reproducibility and transparency.

  • Number of benchmarks: 297
  • Number of contributions: 1671
  • Number of datapoints: 8745168

A brief summary table is given below:

Category/Sub-cat.SinglePropertyPredictionSinglePropertyClassMLFFTextClassTokenClassTextSummaryTextGenAtomGenImageClassSpectraEigenSolver
AI4412111628113321-
ES732--------10-
FF282----------
QC----------6
EXP7--------18-

Example benchmarks

Click on the entries in the Benchmark column. You'll be able to see performance comparison, 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
AISinglePropertyPredictionssub_formula_energyElemNet2_TLMAE0.0924NorthWestern_Universityssub1726
AISinglePropertyPredictiondft_3d_formation_energy_peratomkgcnn_coGNMAE0.0271kgcnndft_3d55713
AISinglePropertyPredictiondft_3d_optb88vdw_bandgapkgcnn_coGNMAE0.1219kgcnndft_3d55713
AISinglePropertyPredictiondft_3d_optb88vdw_total_energykgcnn_coGNMAE0.0262kgcnndft_3d55713
AISinglePropertyPredictiondft_3d_bulk_modulus_kvkgcnn_coNGNMAE8.7022kgcnndft_3d19680
AISinglePropertyClassdft_3d_optb88vdw_bandgapmatminer_xgboostACC0.9361UofTdft_3d55713
AISinglePropertyPredictionqm9_std_jctc_LUMOalignn_modelMAE0.0175ALIGNNqm9_std_jctc130829
AISinglePropertyPredictionhmof_max_co2_adspmatminer_xgboostMAE0.4622UofThmof137651
AIMLFFalignn_ff_db_energyalignnff_pretrained_wt0.1MAE0.0342JARVISalignn_ff_db307111
AIMLFFmlearn_Si_forcesalignnff_mlearn_wt1MULTIMAE0.06942387617720659JARVISmlearn_Si239
AIImageClassstem_2d_image_bravais_classdensenet_modelACC0.8304JARVISstem_2d_image9150
AITextClassarXiv_categoriessvc_model_text_title_abstractACC0.9082ChemNLParXiv100994
FFSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_816_Al2017--Kim-J-S--Pt-Al--LAMMPS--ipr1MAE0.0114IPRdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_816_Alvasp_optcx13MAE0.2JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bulk_modulusvasp_opt86bMAE4.6619JARVISdft_3d21
ESSinglePropertyPredictiondft_3d_bulk_modulus_JVASP_1002_Sivasp_scanMAE0.669JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_bandgapvasp_tbmbjMAE0.4981JARVISdft_3d54
ESSinglePropertyPredictiondft_3d_bandgap_JVASP_1002_Sigpaw_gllbscMAE0.0048GPAWdft_3d1
ESSinglePropertyPredictiondft_3d_epsxvasp_optb88vdw_linoptMAE1.4638JARVISdft_3d16
ESSinglePropertyPredictiondft_3d_Tc_supercon_JVASP_1151_MgB2qe_pbesol_gbrvMAE6.3148JARVISdft_3d1
ESSinglePropertyPredictiondft_3d_Tc_superconqe_pbesol_gbrvMAE3.3785JARVISdft_3d14
ESSinglePropertyPredictiondft_3d_slmevasp_tbmbjMAE5.0925JARVISdft_3d5
ESSpectradft_3d_dielectric_functionvasp_tbmbjMULTIMAE2.8799621766740207JARVISdft_3d4
QCEigenSolverdft_3d_electron_bands_JVASP_816_Al_WTBHqiskit_vqd_SU2_c6MULTIMAE0.002963733593749998JARVISdft_3d1
EXPSpectradft_3d_XRD_JVASP_19821_MgB2bruker_d8MULTIMAE0.020040003548149166MML-BrukerD8dft_3d1
EXPSpectranist_isodb_co2_RM_885210.1007s10450-018-9958-x.Lab01MULTIMAE0.02129168060976213FACTlabnist_isodb1

Help

If you have a question/suggestion, raise a GitHub issue or submit a Google form request.

License

This template is served under the NIST license.
Read the LICENSE file for more info.