The widely accepted intuition that the important properties of solids are determined by a few key variables underpins many methods in physics. Though this reductionist paradigm is applicable in many physical problems, its utility can be limited because the intuition for identifying the key variables often does not exist or is difficult to develop. We have developed a simple, general, and efficient way of finding the key descriptive variables using mathematics that has revolutionized image and signal processing. The key idea is to imagine physics as a "signal" provided by Mother Nature and to use compressive sensing (CS) to recover that signal. Compressive sensing is a powerful paradigm for model building; we find that its models are more physical and predict more accurately than current state-of-the-art approaches and can be constructed at a fraction of the computational cost and user effort.
Experimentally and computationally, the structure of Pt–Cu at 1:3 stoichiometry has a convoluted history. The L1_3 structure has been predicted to occur in binary alloy systems, but has not been linked to experimental observations. Using a combination of electron diffraction, synchrotron X-ray powder diffraction, and Monte Carlo simulations, we found that this phase is present in the Cu–Pt system at 1:3 stoichiometry. We also find that the 4-atom, fcc superstructure L13 is equivalent to the large 32-atom orthorhombic superstructure reported in older literature, resolving much of the confusion surrounding this composition. Monte Carlo simulations confirm the formation of a large cubic superstructure at high temperatures, and its eventual transformation to the L1_3 structure at lower temperature, but also provide evidence of other transitional orderings.
Every technology is intimately related to a particular materials set. The steam engines that powered the industrial revolution in the eighteenth century were made of steel and, information and communication technologies are underpinned by silicon. Once a material is chosen for a given technology, it gets locked with it because of the investments associated with establishing large-scale production lines. This means that changing the materials set in an established technology is a rare event and must be considered as a revolution. Computational materials discovery can play an important role in fueling such revolutions
This is a photo taken in the early morning hours of April 15, 2014 showing the total eclipse of the full moon that marks the start of the Easter season for 2014. This is the first of four total lunar eclipses that will be visible during the next two years. In a rare occurrence, a total lunar eclipse will also happen just before Easter in April of 2015. Photo credit: Dr. Michael D. Joner
This is an image of the Sun secured on January 7, 2014. Solar observers witnessed an active Sun all during the 2013 year and the increased activity has continued into 2014. This picture shows a large sunspot group designated as AR 1944. This is one of the largest and most complex groups of the current solar cycle. It is made up of dozens of individual disturbances each with an intricate magnetic field. This group has an observed size that is greater in extent than the planet Jupiter. This makes this group more than ten times the size of the Earth. Photo credit and processing: Dr. Michael D. Joner
Steve Turley recently published an article titled "Numerical Literacy for Physics Undergraduates" in Computing in Science and Engineering. Click on the image above to read it.
Mimas: Small Moon with a Big Crater : Whatever hit Mimas nearly destroyed it. What remains is one of the largest impact craters on one of Saturn's smallest moons. The crater, named Herschel after the 1789 discoverer of Mimas, Sir William Herschel, spans about 130 kilometers and is pictured above....
This photograph and Description come from NASA's Astronomy Picture of the Day web site.