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Seeing an elusive magnetic effect through the lens of machine learning

Using machine learning, an MIT team detected the magnetic proximity effect in topological materials that can lead to electronics without energy dissipation. The framework is readily transferable to different kinds of problems, such as the superconducting proximity effect in the field of quantum computing. ....

Henry Heiberger , Zhantao Chen , Kangl Wang , Chris Leighton , Mingda Li , Nina Andrejevic , University Of California At Los Angeles , Pennsylvania State University , Mcknight University Professor , National Institute Of Standards , University Of Minnesota , Department Of Nuclear Science , Normanc Rasmussen Career Development Professor , National Institute , Career Development Professor , Nuclear Science , Applied Physics , Leon Fan , Distinguished Professor , Raytheon Chair , Electrical Engineering , Distinguished Mcknight University Professor , Energy Office , Neutron Scattering , Magnetic Proximity Effect , Superconducting Proximity Effect ,

A streamlined approach to determining thermal properties of crystalline solids and alloys

In a September 2020 essay in Nature Energy, three scientists posed several "grand challenges" - one of which was to find suitable materials for thermal. ....

Jaime Fernandez Baca , Zhantao Chen , Tess Smidt , Lawrence Berkeley , Mingda Li , Nina Andrejevic , Nature Energy , Oak Ridge National Laboratory , Argonne National Laboratory , Quantum Matter Group , Lawrence Berkeley National Laboratory , National Science Foundation , Assistant Professor , Nuclear Science , Oak Ridge National , Oak Ridge , Energy Office , Lawrence Berkeley National , Artificial Intelligence , Machine Learning , Energy Storage , ஜெய்ம் பெர்னாண்டஸ் பாக்கா , டெஸ் ஸ்மிட்ட் , லாரன்ஸ் பெர்க்லி , இயற்கை ஆற்றல் , ஓக் ரிட்ஜ் தேசிய ஆய்வகம் ,