Abstract Prof. Billinge has more than 25 years of experience developing and applying techniques to study local structure in materials using x-ray, neutron and electron diffraction including the development of novel data analysis methods including graph theoretic, artificial intelligence and machine learning approaches. He earned his Ph.D in Materials Science and Engineering from University of Pennsylvania in 1992. After 13 years as a faculty member at Michigan State University, in 2008 he took up his current position as Professor of Materials Science and Applied Physics and Applied Mathematics at Columbia University and held a joint position of Physicist at Brookhaven National Laboratory between 2008 and 2022. Prof. Billinge has published more than 350 papers in scholarly journals. He is a fellow of the American Physical Society and the Neutron Scattering Society of America, a former Fulbright and Sloan fellow and has earned a number of awards including the 2022 Distinguished Powder Diffractionist Prize of the European Powder Diffraction Conference, the 2018 Warren Award of the American Crystallographic Association and being honored in 2011 for contributions to the nation as an immigrant by the Carnegie Corporation of New York.29Professor of Materials Science and of Applied Physics and of Applied Mathematics, At the heart of materials science studies for next generation materials is an idea that we want to be studying real materials doing real things, often in real devices. In practice, this presents a number of key data analysis and interpretation challenges because it implies we are studying ever more complicated samples, often in complex heterogeneous environments and in time-resolved operando setups, and we are interrogating our data for more and more subtle effects such as microstructures and evolving defects and local structures. Advanced data analysis algorithms and software are essential for the success of this enterprise. Of particular interest is the study of nanomaterials and materials structure on different lengthscales. In this talk I will describe various developments that leverage latest data acquisiont and analysis techniques, sometimes powered by artificial intelligence (AI) and machine learning (ML), that reveal how materials behave on different length-scales and sometimes also timescales. The materials studied include materials for sustainable energy, environmental remediation, and cultural heritage studies, and techniques range from spatially resolved x-ray and electron nanostrucuture studies and neutron diffraction and scattering.Department of Applied Physics and Applied Mathematics Figure caption: Schematic of a wave of structural reorganization that spreads out from a localized photo excitation even in a quantum material studied with ultrafast PDF.and Time-scales Simon J. L. Billinge Real Materials in Action: Studying Structure on Different LengthInvited Talk: S1-6
元のページ ../index.html#29