Machine-learning models of matter beyond interatomic potentials

Combining electronic structure calculations and machine learning (ML) techniques has become a common approach in the atomistic modeling of matter. Using the two techniques together has allowed researchers, for instance, to ...

Machine learning improves particle accelerator diagnostics

Operators of the primary particle accelerator at the U.S. Department of Energy's Thomas Jefferson National Accelerator Facility are getting a new tool to help them quickly address issues that can prevent it from running smoothly. ...

DeepTFactor predicts transcription factors

A joint research team from KAIST and UCSD has developed a deep neural network named DeepTFactor that predicts transcription factors from protein sequences. DeepTFactor will serve as a useful tool for understanding the regulatory ...

Polymer to capture ammonia pollution realized

Researchers at the Niels Bohr Institute and the Department of Chemistry at University of Copenhagen, have recently designed a porous polymer aiming for the capture of small molecules. Ammonia is a toxic gas widely used as ...

Developing smarter, faster machine intelligence with light

Researchers at the George Washington University, together with researchers at the University of California, Los Angeles, and the deep-tech venture startup Optelligence LLC, have developed an optical convolutional neural network ...

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