Understanding the phase preferences of transition metal dichalcogenides - Pratibha Dev

Transition metal dichalcogenides (TMDs), a chemically distinct class of layered quantum materials, adopt one of several crystal structures. Since different structures/phases possess dramatically different electronic structure properties, an important question to answer is:
What dictates the observed phase preferences of TMDs? In fact, this has remained an open question for nearly six decades. A powerful combination of techniques – high-throughput quantum mechanical computations and machine learning tools – not only helped us to rediscover what was already known through works of earlier researchers, but also revealed other factors that were previously not known to influence the structural preferences of TMDs. This work demonstrates how machine learning can be used to tackle old and new problems in Solid State Physics and Chemistry.

Pratibha Dev is a Professor of Physics at the Howard University, Washington D.C. Following her
Ph.D. in theoretical Condensed Matter Physics from the University at Buffalo (Buffalo, NY) in
2009, she worked as an EMPOWER fellow (Irish Research Council fellowship) in University College
Dublin, Ireland (2010-2012). She then joined Naval Research Laboratory in Washington DC as a
National Research Council fellow (2012-2015) before joining the Howard University. In her
computational research, which spans the fields of Condensed Matter Physics, Material Science
and Quantum Chemistry, she focuses on determining structure-property-function relationships
in bulk and nanoscale materials. Of particular interest are studies of advanced functionalities in
novel two-dimensional quantum materials, topologically non-trivial materials, and spin-active
quantum emitters in wide bandgap semiconductors for quantum technologies. Theoretical
techniques used in her group include state-of-the-art computational methods, such as density
functional theory and machine learning techniques.

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