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Abstract Heteroanionic oxysulfide perovskite compounds represent an emerging class of new materials allowing for a wide range of tunability in the electronic structure that could lead to a diverse spectrum of novel and improved functionalities. Unlike cation ordered double perovskites—where the origins and design rules of various experimentally observed cation orderings are well known and understood—anion ordering in heteroanionic perovskites remains a largely uncharted territory. In this contribution, we present and discuss insights that have emerged from our first-principles-based electronic structure analysis of a prototypical anion-ordered SrHf(O_0.5S_0.5)₃oxysulfide chemistry, studied in all possible anion configurations allowed within a finite size supercell. We demonstrate that the preferred anion ordering is always an all-cisarrangement of anions around an HfO₃S₃octahedron. As a general finding beyond the specific chemistry, the origins of this ordering tendency are traced back to a combined stabilization effect stemming from electronic, elastic, and electrostatic contributions. These qualitative notions are also quantified using state-of-the-art machine learning models. We further study the relative stability of the identified ordering as a function of A (Ca, Sr, Ba) and B (Ti, Zr, Hf) site chemistries and probe chemistry-dependent trends in the electronic structure and functionality of the material. Most remarkably, we find that the identified ground-state anion ordering breaks the inversion symmetry to create a family of oxysulfide ferroelectrics with a macroscopic polarization >30 μC/cm², exhibiting a significant promise for electronic materials applications. 

Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .