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AI method radically speeds predictions of materials’ thermal properties
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
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Our work accepted by Applied Physics Review. Big congratulations!
Our work “Precise Fermi-level engineering in a topological Weyl semimetal via fast ion implantation,” led by Manasi and Abhijatmedhi, has been accepted by Applied Physics Review. In this work, we show that the state-of-the-art accelerator…
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Our work accepted by Nature Computational Science. Big Congratulations!
Our work “Virtual Node Graph Neural Network for Full Phonon Prediction,” led by our group members Ryotaro, Abhijatmedhi, and Artittaya, supported by ORNL Scientist YQ and many others, is accepted by Nature Computational Science. This work overturns…
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Abhijatmedhi has received EECS best TA award. Big Congratulations!!
Abhijatmedhi has received the prestigious 2024 Harold L. Hazen Teaching Award for excellence in EECS. Big congratulations!!
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Our work accepted by Matter. Big congratulations!
Our work “Machine Learning Detection of Majorana Zero Modes from Zero Bias Peak Measurements”, led by our team members Mouyang, supported by Ryotaro and Earth, has been accepted by Matter, the leading physics journal from…
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Two MIT teams selected for NSF sustainable materials grants
Chosen from 16 finalist teams, the MIT-led projects will investigate quantum topological materials and sustainable microchip production.
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Abhijatmedhi has been selected to attend the National NX School. Big congratulations!
Our group member Abhijatmedhi has been selected to attend the 26th National School on Neutron and X-ray Scattering, at Argonne National Laboratory and Oak Ridge National Labratory. Big congratulations!
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With inspiration from “Tetris,” MIT researchers develop a better radiation detector
The device, based on simple tetromino shapes, could determine the direction and distance of a radiation source, with fewer detector pixels.
