JARVIS-Leaderboard: a large scale benchmark of materials design methods

K Choudhary and D Wines and KM Li and KF Garrity and V Gupta and AH Romero and JT Krogel and K Saritas and A Fuhr and P Ganesh and PRC Kent and KQ Yan and YC Lin and SW Ji and B Blaiszik and P Reiser and P Friederich and A Agrawal and P Tiwary and E Beyerle and P Minch and TD Rhone and I Takeuchi and RB Wexler and A Mannodi-Kanakkithodi and E Ertekin and A Mishra and N Mathew and M Wood and AD Rohskopf and J Hattrick-Simpers and SH Wang and LEK Achenie and HL Xin and M Williams and AJ Biacchi and F Tavazza, NPJ COMPUTATIONAL MATERIALS, 10, 93 (2024).

DOI: 10.1038/s41524-024-01259-w

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

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