Opening Remarks

Product and Process Analysis by High-throughput X-ray Absorption Spectroscopy: From Concept to Reality

• Sven L. M. Schroeder, University of Leeds

An Iterative Machine Learning – High-throughput Experimental Approach to Discovering Novel Amorphous Alloys

• Jason Hattrick-Simpers, National Institute of Standards and Technology

Machine Learning for Automated X-ray Scattering Experiments

• Kevin Yager, Center for Functional Nanomaterials

Extracting Nanoscale Details from X-ray Absorption Data by Supervised Machine Learning

• Janis Timoshenko, Stony Brook University

Coffee Break (Included) and Group Photo

Combining Atomistic Modeling and Machine Learning for Co-refinement of x-ray and Electron Characterization Data

• Maria Chan, Argonne National Laboratory

X-ray Spectroscopy Theory, Data Base Mining, and Bayesian Analysis

• John Rehr, University of Washington

Prospects for Elucidating Reaction Mechanisms via Adaptive Transient Kinetics Experiments

• Andrew Medford, Georgia Tech

Coffee Break

The Materials Project: Conception to Confirmation in a Virtual Lab

• Shyam Dwaraknath, Lawrence Berkeley National Laboratory

Data Management and Data-Enabled Research at NIST

• Robert Hanisch, National Institute of Standards and Technology

Provenance-enabled Sample Measurements and Tracking for Multi-modal Analysis and Predictive Synthesis

• Line Pouchard, Computational Science Initiative

Summary and Discussion