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Abstract We developed a physical model to fundamentally understand the conductive filament (CF) formation and growth behavior in the switching layer during electroforming process in the metal-oxide-based resistive random-access memories (RRAM). The effects of the electrode and oxide layer properties on the CF morphology evolution, current-voltage characteristic, local temperature, and electrical potential distribution have been systematically explored. It is found that choosing active electrodes with lower oxygen vacancy formation energy and oxides with small Lorenz number (ratio of thermal and electrical conductivity) enables CF formation at a smaller electroforming voltage and creates a CF with more homogeneous morphology. This work advances our understanding of the kinetic behaviors of the CF formation and growth during the electroforming process and could potentially guide the oxide and electrode materials selection to realize a more stable and functional RRAM. 

Abstract 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/ 

Ferroelectric materials such as barium titanate (BaTiO3) have a wide range of applications in nano scale electronic devices due to their outstanding properties. In this study, we developed an easily extendable atomistic ReaxFF reactive force field for BaTiO3 that can capture both its field- as well as temperature-induced ferroelectric hysteresis and corresponding changes due to surface chemistry and bulk defects. Using our force field, we were able to reproduce and explain a number of experimental observations: (1) existence of a critical thickness of 4.8 nm below which ferroelectricity vanishes in BaTiO3; (2) migration and clustering of oxygen vacancies (OVs) in BaTiO3 and reduction in the polarization and the curie temperature due to the OVs; (3) domain wall interaction with surface chemistry to influence ferroelectric switching and polarization magnitude. This new computational tool opens up a wide range of possibilities for making predictions for realistic ferroelectric interfaces in energy-conversion, electronic and neuromorphic systems.