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The past few years have witnessed a dramatic increase of the pace of application of advanced data science methods such as machine learning to digital microstructures and related properties. In addition, in situ and in operando experimental techniques to characterize 3D microstructures and measure time-dependent evolution under applied fields are providing completely new research platforms to quantify deformation and damage mechanisms in alloy systems. The field of Physical Metallurgy is rapidly evolving and in many respects is leading the way at the intersection of these new technologies in emerging paradigms such as Integrated Computational Materials Engineering and the U.S. Materials Genome Initiative. This confluence of science and technology advances related to the digital frontier of materials informatics is expected to broadly and fundamentally transform both the education and research and development enterprises. A primary goal of this Gordon Research Conference is to explore demonstrated advances in coupling computation, data science and experiments to enhance our depth of scientific understanding and the rate of its acquisition. Another aim is to explore potential intersections of emerging technologies and methods to frame essential questions regarding how these intersections might further develop and how collaborative efforts might be configured to take full advantage of the power of their coupling. Issues regarding complexity of relating material structure with associated properties or responses are at the core of modern Physical Metallurgy. These issues demand much deeper and quantitative understanding of fundamental unit processes and mechanisms that control thermodynamic driving forces and kinetics of microstructure evolution for various applications. This is essential to support materials design or improvement. Grain boundary complexity will be pursued as a session topic owing to its expression of high dimensionality of structure and structure-response relations. High entropy alloys or complex multi-component alloys offer a fruitful emergent class of materials which are quite promising, and yet deformation and failure mechanisms are not fully understood. Other sessions will focus on how data science adds value to materials discovery, including applications of machine learning to alloy discovery, design, and development, as well as recent advances in in situ and in operando experimental methods such as TEM, x-ray diffraction tomography, and phase contrast tomography. Foundational principles of thermodynamics and kinetics will weave as a connective thread throughout the presentations and discussions at oral and poster sessions.