SMART Breakthrough in Materials Discovery Enables ‘Twistronics’ for Bulk Systems
Written by AZoNanoApr 22 2021
Researchers from the Low Energy Electronic Systems (LEES) Interdisciplinary Research Group (IRG) at Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore together with Massachusetts Institute of Technology (MIT) and National University of Singapore (NUS) have discovered a new way to control light emission from materials.
SMART researchers show the phenomena related to the formation of moiré superlattices observed in monolayer-based two-dimensional systems can be translated to tune optical properties of three-dimensional, bulk-like hexagonal boron nitride, even at room temperature. Photo Credit: Nano Letters Cover, Volume 21, Issue 7
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SMART breakthrough in materials discovery enables twistronics for bulk systems
The findings allow manipulation of materials for the first time by stacking films at a twist angle, allowing a new way to control light emitting from materials
Recent discoveries focused on manipulation of atomically-thin 2D materials, while the new breakthrough can be used to stack technologically-relevant 3D materials at a twist angle
Method allows continuous, systematic control of optical emission intensity and energy, and can produce ultraviolet emissions at room temperature for bulk systems
The discovery can be significant for applications in medicine, environmental or information technologies.
Researchers from the Low Energy Electronic Systems (LEES) Interdisciplinary Research Group (IRG) at Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore together with Massachusetts Institute of Technology (MIT) and National University of Singap
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IMAGE: A comparison summary of deep neural networks (DNNs) and discrete choice models (DCMs) characteristics view more
Credit: Singapore-MIT Alliance for Research and Technology (SMART)
Singapore, 19 April, 2021 - Researchers at the Future Urban Mobility (FM) Interdisciplinary Research Group (IRG) at Singapore-MIT Alliance for Research and Technology (SMART), MIT s research enterprise in Singapore, have created a synthetic framework known as theory-based residual neural network (TB-ResNet), which combines discrete choice models (DCMs) and deep neural networks (DNNs), also known as deep learning, to improve individual decision-making analysis used in travel behaviour research.
In this research paper, Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks, recently published in established transportation science journal
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