Quantum chemistry, the study of chemical properties and processes at the quantum scale, has opened many paths to research and discovery in modern chemistry. Without ever handling a beaker or a test tube, chemists can make predictions about the properties of a given atom or molecule and how it will undergo chemical reactions by studying its electronic structure--how its electrons are arranged in orbitals--and how those electrons interact with those of other compounds or atoms. However, as powerful as quantum chemistry has shown itself to be, it also has a big drawback: Accurate calculations are resource-intensive and time consuming, with routine chemical studies involving computations that take days or longer. Now, thanks to a new quantum chemistry tool that uses machine learning, quantum-chemistry calculations can be performed 1,000 times faster than previously possible, allowing accurate quantum chemistry research to be performed faster than ever before. The tool, called OrbNet, was developed through a partnership between Caltech's Tom Miller, professor of chemistry, and Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences.

In their work to unearth evidence of fraudulent activities, forensic accounting investigators dig through diverse data looking for anomalies that suggest something is just not right. But as the massive volumes of data collected by companies balloon, this task has become increasingly arduous, time-consuming and humanly impossible. Instead of investigators manually reviewing spreadsheet rows and columns, looking for three or four data elements that together indicate a suspicious transaction, ML can peruse thousands of data elements -- instantly. The regrettable consequence is the greater chance of a well-thought-out scam slipping through the cracks. A case in point is healthcare fraud, which has been estimated to cost the United States tens of billions of dollars annually.

In their work to unearth evidence of fraudulent activities, forensic accounting investigators dig through diverse data looking for anomalies that suggest something is just not right. But as the massive volumes of data collected by companies balloon, this task has become increasingly arduous, time-consuming and humanly impossible. Instead of investigators manually reviewing spreadsheet rows and columns, looking for three or four data elements that together indicate a suspicious transaction, ML can peruse thousands of data elements -- instantly. The regrettable consequence is the greater chance of a well-thought-out scam slipping through the cracks. A case in point is healthcare fraud, which has been estimated to cost the United States tens of billions of dollars annually.

Researchers have created a new metallic glass that is stronger and lighter than some of the best steel, while standing up to corrosion and wear. A team from the Department of Energy's SLAC National Accelerator Laboratory, the National Institute of Standards and Technology (NIST) and Northwestern University used a new system at SLAC's Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning with experiments that quickly make and screen hundreds of sample materials at a time. This allowed the researchers to discover three new blends of ingredients that form metallic glass about 200 times faster than before. While there are millions of possible combinations of ingredients for metallic glass, only a few thousand combinations have been evaluated in the last 50 years, with only a select few being developed to the point where they can be commercially useful. "It typically takes a decade or two to get a material from discovery to commercial use," Chris Wolverton, the Jerome B. Cohen Professor of Materials Science and Engineering in Northwestern's McCormick School of Engineering, said in a statement.