Engineering topics

Mandal, Subhasish

Publications and source records attributed to Mandal, Subhasish.

3 records

DOE OSTI2024

Spin-selective evolution of the Zhang-Rice state in binary transition metal oxide MnO(001) film

Here, the Zhang-Rice (ZR) state is a strongly hybridized bound state formed by transition-metal and oxygen atoms. The spin fluctuations within the ZR state are known to play an important role in high-T c superconductivity in cuprates. Here, we employ a combination of angle-resolved photoemission spectroscopy (ARPES), x-ray photoemission spectroscopy (XPS), and ab initio embedded dynamical mean-field theory (eDMFT) to investigate the influence of magnetic ordering on the spectral characteristics of the valence band and Mn 2⁢p core-level in MnO (001) ultrathin films. Our results demonstrate that a complex spin-selective evolution of Mn 3⁢d-O2⁢p hybridization develops due to the long-range antiferromagnetic (AFM) ordering. This hybridization significantly alters the spectral shape and weight of the ZR state. Specifically, in the AFM phase, we observed the sharpening of the ZR state and band folding with the periodicity of the AFM unit cell of MnO(001). We also demonstrated a strong connection between the spectral evolution of the ZR state and the non-local screening channels of the photoexcited core holes. Further, our detailed temperature-dependent study reveals the presence of short-range antiferromagnetic correlations that exist at much higher temperatures than Neel temperature (T N ) and shows the evolution of the ZR state across the magnetic transitions and its implication to the core-hole screening in 3⁢d binary transition metal oxides.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Neuromorphic learning with Mott insulator NiO

Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. In this work, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia, habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability–plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.

42 ENGINEERING↗

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↗