Oral Defense of Doctoral Dissertation – Computational Sciences and Informatics – Atomistic Monte Carlo Simulation and Machine Learning Data Analysis of Eutectic Alkali Metal Alloys – Doug Reitz
Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Sciences and Informatics
Department of Computational and Data Sciences
College of Science
George Mason University
Bachelor of Science, Pennsylvania State University, 1995
Master of Science, Binghamton University, 2007
Atomistic Monte Carlo Simulation and
Machine Learning Data Analysis of
Eutectic Alkali Metal Alloys
Tuesday, November 27, 2018, 10:00 a.m.
Research Hall, Room 92
All are invited to attend.
Estela Blaisten-Barojas, Dissertation Director
Combining atomistic simulations and machine learning techniques can significantly expedite the materials discovery process. Here an application of such methodological combination for the prediction of the configuration phase (liquid, amorphous solid, and crystalline solid), melting transition, and amorphous-solid behavior of three eutectic alkali metal alloys (Na-K, Na-Cs, K-Cs) is presented. It is shown that efficient prediction of these properties is possible via machine learning methods trained on the topological local structural properties alone. The atomic configurations resulting from Monte Carlo annealing of the eutectic alkali alloys are analyzed with topological attributes based on the Voronoi tessellation using expectation-maximization clustering, Random Forest classification, and Support Vector Machine classification. It is shown that the Voronoi topological fingerprints make an accurate and fast prediction of the alloy thermal behavior by cataloging the atomic configurations into three distinct phases: liquid, amorphous solid, and crystalline solid. Using as few as eight topological features the configurations can be categorized into these three phases. With the proposed metrics, arrest-motion and melting temperature ranges are identified through a top down clustering of the atomic configurations cataloged as amorphous solid and liquid.
The methodology presented here is of direct relevance in identifying or screening unknown materials in a targeted class with desired combination of topological properties in an efficient manner with high fidelity. The results demonstrate explicitly the exceptional power of domain-based machine learning in discovering topological influence on thermodynamic properties, and at the same time providing valuable guidance to machine learning workflows for the analysis of other condensed systems. This statistical learning paradigm is not restricted to eutectic alloys or thermodynamics, extends the utility of topological attributes in a significant way, and harnesses the discovery of new material properties.