Finding fire and ice: Modeling the probability of methane hydrate deposits on the seafloor

A team of researchers from Sandia National Laboratories and the U.S. Naval Research Laboratory have developed a new system to model the likelihood of finding methane hydrate and methane gas that was tested in a region of seafloor off the coast of North Carolina. This test was published on March 14 in the scientific journal Geochemistry, Geophysics, Geosystems.

Read more

Alexa, do I have an irregular heart rhythm? First AI system for contactless monitoring of heart rhythm using smart speakers

University of Washington researchers have developed a new skill for a smart speaker that for the first time monitors both regular and irregular heartbeats without physical contact.

Read more

Machine Learning Trims Tuning Time for Electron Beam by 65 Percent

Linear accelerator operators use computer algorithms to automate some parts of the machine tuning process. These algorithms make fast decisions, but they have not previously incorporated fundamental physics or learned from past mistakes. A new machine learning algorithm learns both from experience and physics simulations to reduce the time needed for a part of the machine tuning process by 65 percent.

Read more

Scientists voice concerns, call for transparency and reproducibility in AI research

In an article published in Nature on October 14, 2020, scientists at Princess Margaret Cancer Centre, University of Toronto, Stanford University, Johns Hopkins, Harvard School of Public Health, Massachusetts Institute of Technology, and others, challenge scientific journals to hold computational researchers to higher standards of transparency, and call for their colleagues to share their code, models and computational environments in publications.

Read more

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.

Read more

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.

Read more

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.

Read more

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.

Read more

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.

Read more