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DTSTART;TZID=America/New_York:20200220T104500
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SUMMARY:MSE Faculty Candidate Seminar: "Uncovering atomistic mechanisms of crystallization using Machine Learning"
DESCRIPTION:Solid-liquid interfaces have notoriously haphazard atomic environments. While essentially amorphous\, the liquid has short-range order and heterogeneous dynamics. The crystal\, albeit ordered\, contains a plethora of defects ranging from adatoms to dislocation-created spiral steps. All these elements are of paramount importance in the crystal growth process\, which makes the crystallization kinetics challenging to describe concisely in a single framework. In this seminar I will introduce a novel data-driven approach to systematically detect\, encode\, and classify all atomic-scale crystallization mechanisms described above. I will also show how this approach naturally leads to a predictive kinetic model of crystallization that takes into account the entire zoo of microstructural elements present at solid-liquid interfaces. In this innovative application of data science to materials Machine Learning is employed as an aid to augment human intuition\, rather than a substitute thereof. The result is an approach that blends prevailing scientific methods with data-science tools to produce physically-consistent models and conceptual knowledge.
URL:https://seasevents.nmsdev7.com/event/mse-faculty-candidate-seminar-uncovering-atomistic-mechanisms-of-crystallization-using-machine-learning/
LOCATION:Auditorium\, LRSM Building\, 3231 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
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