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/

We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form $$\hbox {A}_2\hbox {B}_2\hbox {X}_6$$A 2 B 2 X 6 , based on the known material $$\hbox {Cr}_2\hbox {Ge}_2\hbox {Te}_6$$Cr 2 Ge 2 Te 6 , using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.

Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials.

Two-dimensional (2D) hybrid organic-inorganic perovskite (HOIP) crystals show promise as scintillating materials for wide-energy radiation detection, outperforming their three-dimensional counterparts. In this study, we synthesized single crystals of (PEA_2-xBZA_x)PbBr₄ (x ranging from 0.1 to 2), utilizing phenethylammonium (C₆H₅CH₂CH₂NH₃⁺) and benzylammonium (C₆H₅CH₂NH₃⁺) cations. These materials exhibit favorable optical and scintillation properties, rendering them suitable for high light yield (LY) and fast-response scintillators. Our investigation, employing various techniques such as X-ray diffraction (XRD), photoluminescence (PL), time-resolved (TR) PL, Raman spectroscopy, radioluminescence (RL), thermoluminescence (TL), and scintillation measurements, unveiled lattice strain induced by dual-organic cations in powder X-ray diffraction. Density functional theory analysis demonstrated a maximal 0.13 eV increase in the band gap with the addition of BZA cation addition. Notably, the largest Stokes shift of 0.06 eV was observed in (BZA)₂PbBr₄. The dual-organic cation crystals displayed >80% fast component scintillation decay time, which is advantageous for the scintillating process. Furthermore, we observed a dual-organic cations-induced enhancement of electron-hole transfer efficiency by up to 60%, with a contribution of >70% to the fast component of scintillation decay. The crystal with the lowest BZA concentration, (PEA_1.9BZA_0.1)PbBr₄, demonstrated the highest LYs of 14.9 ± 1.5 ph/keV at room temperature. Despite a 55-70% decrease in LY for BZA concentrations >5%, simultaneous reductions in scintillation decay time (12-32%) may work for time-of-flight positron emission tomography and photon-counting computed tomography. Our work underscores the crucial role of dual-organic cations in advancing our understanding of 2D-HOIP crystals for materials science and radiation detection applications.