11.4.20-MRL-Digital-Welcome-Schuh

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  Over the past several decades the iterative trial-and-error approach to alloy design has become dramatically ‘digitally enhanced’.  Physically-motivated computational models that incorporate thermodynamics, kinetics, and processing pathways can substantially narrow the search for optimum alloy compositions and configurations, while high-throughput experimental methods accelerate iteration. In advanced research areas where the controlling physics are not always known, computation can be augmented with data science and machine learning methods to span vast compositional spaces where few experiments exist. This talk will highlight projects of MIT faculty contributing to the digital transformation of the innovative ‘front-end’ of the metals industry—the design and reduction-to-practice of new alloys.

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