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HEATER SEMINAR SERIES: Dr. Albert Mar

High-Throughput Approaches for Discovering Thermoelectric Materials
When Oct 26, 2018
from 02:00 PM to 03:00 PM
Where C2-361 University of Waterloo
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Traditional approaches to search for new solid state materials can involve systematic investigations, serendipitous discoveries, or, for limited classes of compounds, rational strategies for manipulating building blocks.  Answering the call of the Materials Genome Initiative,1launched in 2011, to “discover, develop, and deploy new materials twice as fast,” we are applying high-throughput methods to predict the structures of new compounds and optimize properties of materials.  In collaboration with Citrine Informatics,2we have been using machine-learning methods to search for unconventional candidates that are likely to exhibit low thermal conductivity, an important criterion for good thermoelectric materials.  In collaboration with the Materials Project,3we have also been exploiting computational studies to identify new candidates for thermoelectric materials, such as inverse perovskites and layered phosphides.

1.National Science and Technology Council, Materials Genome Initiative Strategic Plan, 2014.2.Citrine Informatics, www.citrination.com.   3.The Materials Project, www.materialsproject.org.

 

Dr. Arthur Marreceived a Ph.D. from Northwestern University in 1992 under the supervision of James A. Ibers.  He worked as an NSERC Postdoctoral Fellow in the laboratory of Yves Piffard and Jean Rouxel at the Institut des Matériaux de Nantes in 1993–1994.  He is currently a full Professor in the Department of Chemistry at the University of Alberta.  He is considered to be one of the leading experts in the field of inorganic solid state chemistry, having established an internationally recognized research program encompassing the synthesis, characterization, and applications of intermetallic compounds and Zintl phases, with an aim to understand structure-property relationships.  In recent years, he has been at the forefront of applying machine-learning approaches to materials discovery.  

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