About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Uncovering Atomistic Mechanisms of Crystallization Using Machine Learning |
Author(s) |
Rodrigo Freitas, Evan J. Reed |
On-Site Speaker (Planned) |
Rodrigo Freitas |
Abstract Scope |
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 mechanisms of crystallization. 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. The result is an approach that blends prevailing scientific methods with data-science tools to produce physically-consistent models and novel conceptual knowledge. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, Solidification |