Making a Material World Better, Faster Now: Q&A With Materials Project Director Kristin Persson

Berkeley Lab’s Kristin Persson shares her thoughts on what inspired her to launch the Materials Project online database, the future of materials research and machine learning, and how she found her own way into a STEM career.

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Computers scour satellite imagery to unveil Madagascar’s mysteries

Scientists may be a step closer to solving some of anthropology’s biggest mysteries thanks to a machine learning algorithm that can scour through remote sensing data, such as satellite imagery, looking for signs of human settlements, according to an international team of researchers.

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CFN Staff Spotlight: Xiaohui Qu Bridges the Data Science-Materials Science Gap

As a staff member in the Theory and Computation Group at Brookhaven Lab’s Center for Functional Nanomaterials, Qu applies various approaches in artificial intelligence to analyze experimental and computational nanoscience data.

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Machine learning technique speeds up crystal structure determination

A computer-based method could make it less labor-intensive to determine the crystal structures of various materials and molecules, including alloys, proteins and pharmaceuticals. The method uses a machine learning algorithm, similar to the type used in facial recognition and self-driving cars, to independently analyze electron diffraction patterns, and do so with at least 95% accuracy.

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Department of Energy Announces $21.4 Million for Quantum Information Science Research

The following news release was issued on Aug. 26, 2019 by the U.S. Department of Energy (DOE). It announces funding that DOE has awarded for research in quantum information science related to particle physics and fusion energy sciences. Scientists at DOE’s Brookhaven National Laboratory are principal investigators on two of the 21 funded projects.

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