As the name implies, crystallography requires crystals – specifically, purified samples of the molecule of interest, coaxed into a crystal form. But most molecules form powders composed of jumbled granules, not picture-ready crystals. A new computer algorithm, combined with a state-of-the-art laser, can adapt X-ray crystallography for the many not-so-neat-and-tidy compounds that scientists seek to study.
A team of researchers at Berkeley Lab used a quantum computer to successfully simulate an aspect of particle collisions that is typically neglected in high-energy physics experiments, such as those that occur at CERN’s Large Hadron Collider.
A Berkeley Lab intern and his mentor develop an algorithm that will extract better structures from low-quality crystallography diffraction data
The Berkeley Lab-led center will forge the technological solutions needed to harness quantum information science for discoveries that benefit the world. It will also energize the nation’s research community to ensure U.S. leadership in quantum R&D and accelerate the transfer of technologies from the lab to the marketplace.
Giant-scale physics experiments are increasingly reliant on big data and complex algorithms fed into powerful computers, and managing this multiplying mass of data presents its own unique challenges. To better prepare for this data deluge posed by next-generation upgrades and new experiments, physicists are turning to the fledgling field of quantum computing.
Berkeley Lab’s Chris Mungall and Nomi Harris explain how agreeing on precise definitions of each rare disease can lead to more accurate diagnoses and better treatments, and share new evidence showing this endeavor is more important than ever.
Katherine Yelick, the Associate Laboratory Director for Computing Sciences at Berkeley Lab and professor of electrical engineering and computer sciences at UC Berkeley, has been honored by HPCwire as their Editor’s Choice for Outstanding Leadership in high-performance computing.
A team of researchers at Berkeley Lab and UC Berkeley has successfully demonstrated how machine-learning tools can improve the stability of light beams’ size for science experiments at a synchrotron light source via adjustments that largely cancel out unwanted fluctuations.
Berkeley Lab’s ESnet is one of five organizations leading an effort to create a nationwide research infrastructure that will enable the computer science and networking community to develop and test novel architectures that could yield a faster, more secure Internet.
An international team of scientists that includes Berkeley Lab researchers has announced a breakthrough in its quest to measure the mass of the neutrino, one of the most abundant yet elusive elementary particles in our universe.