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Our work is accepted by Matter. Congratulations!
Our work “A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra” is accepted by Matter (Cell Press). By using AI to augment neutron vibrational spectroscopy, this work represents a significant step forward, enabling the…
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Our work is accepted by Nano Letters. Congratulations!
Our work “Tuning Chiral Anomaly Signature in a Dirac Semimetal via Fast-Ion Implantation” has been accepted by Nano Letters. Congratulations!
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Why some quantum materials stall while others scale
In a new study, MIT researchers evaluated quantum materials’ potential for scalable commercial success — and identified promising candidates.
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New tool makes generative AI models more likely to create breakthrough materials
With SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.
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Our work is accepted by Materials Today. Congratulations!
Our work “Are Quantum Materials Economically and Environmentally Sustainable?” is accepted by Materials today. In this work, using data-mining and AI, we found an interesting positive correlation between the “quantumness” and their cost and environmental…
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Our work is accepted by Materials Today Physics. Congratulations!
Our theory work “Theory of the photomolecular effect,” is accepted by Materials Today Physics. In the work we developed a microscopic quantum mechanical model to explain how the visible light can cleave water clusters and…
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Our another work is accepted by Nature Materials. Congratulations!
Our work “AI-driven Approaches for Materials Design and Discovery” is accepted by Nature Materials. Congratulations!
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Our work is accepted by Nature Materials. Congratulations!
Our work, “Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates” (SCIGEN), has been accepted by Nature Materials. Congratulations!
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Theory-guided strategy expands the scope of measurable quantum interactions
An oft-ignored effect can be used to probe an important property of semiconductors, a new study finds.
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Zhantao joins UT Austin as an Assistant Professor. Congratulations!
Our Group alumnus, Dr. Zhantao Chen, joins UT Austin as a tenure-track Assistant Professor. His PhD thesis is “Machine Learning for Phonon Thermal Transport.” Big congratulations!
