Engineering topics

Rabe, Karin

Publications and source records attributed to Rabe, Karin.

3 records

DOE OSTI2024

Prediction of BiS2-type pnictogen dichalcogenide monolayers for optoelectronics

Abstract In this work, we introduce a 2D materials family with chemical formula MX 2 (M={As, Sb, Bi} and X={S, Se, Te}) having a rectangular 2D lattice. This materials family has been predicted by systematic ab-initio structure search calculations in two dimensions. Using density-functional theory and many-body perturbation theory, we study the structural, vibrational, electronic, optical, and excitonic properties of the predicted MX 2 family. Our calculations reveal that the predicted SbX 2 and BiX 2 monolayers are stable while the AsX 2 layers exhibit an in-plane ferroelectric instability. All materials display strong excitonic effects and good optical absorption within the infrared-to-visible range. Hence, these monolayers can harvest solar energy and serve in optoelectronics applications. Furthermore, our results indicate that exfoliation of the predicted MX 2 monolayers from their bulk counterparts is experimentally viable.

Materials Science↗

DOE OSTI2020

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

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 .

36 MATERIALS SCIENCE↗

DOE OSTI2020

Quantum Matter: Computation Meets Experiments

The past few years have seen tremendous advances in our understanding of strongly interacting quantum systems. By combining progress in theoretical concepts and methods with algorithmic advances, computational methods have shed new light on key open questions in the physics of quantum matter, both for materials with strong electronic correlations and for interacting quantum gases. At the same time, new materials and new techniques have greatly increased the range of experimental information available. This Aspen Winter Conference will bring leading theorists with expertise in a broad range of computational methods together with experimentalists to discuss the potential of new methods, the accomplishments of existing methods and opportunities for future experiment/ theory collaboration. The meeting will focus on a broad set of physics questions of current interest in the field, for which computational methods and experiments have brought or have the potential to bring new insights.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗