BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Missouri State University/Calendar of Events//EN CALSCALE:GREGORIAN X-WR-TIMEZONE:America/Chicago BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:15179f50-4641-4dd5-88e1-f677fa9afdc3.221849@calendar.missouristate.edu CREATED:20220819T233614Z LAST-MODIFIED:20220819T233614Z LOCATION:Kemper Hall 206 SUMMARY:Chemistry & PAMS Seminar: "Materials Discovery through Machine Lea rning: Experimental Validation and Interpretable Models" by Dr. Arthur Mar DESCRIPTION:Dr. Arthur MarDepartment of ChemistryUniversity of Alberta\n\n \nAbstract:Machine learning algorithms have been applied successfully in many areas of materials chemistry.  An ongoing challenge is make accurate predictions of the crystal structures of inorganic solids\, their site p references\, and their physical properties.  We have previously developed machine learning models to predict structures within the large family of intermetallic compounds known as Heusler compounds (used as thermoelectr ic materials\, ferromagnets\, magnetocaloric materials\, and catalysts)\, followed by experimental validation.  Nevertheless\, skeptics rightfully criticize many of these models as being too “black box\,” with little ch emical insight and explainability.  We demonstrate our efforts to generat e more interpretable machine learning models\, using the structures of bi nary rare-earth intermetallics RX as an example\, to illustrate that it i s possible to gain insight and practical guidance to prepare new material s.\n\n\nBio:Dr. Arthur Mar received a Ph.D. from Northwestern University in 1992 under the supervision of James A. Ibers.  He worked as an NSERC P ostdoctoral Fellow in the laboratory of Yves Piffard and Jean Rouxel at t he 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 a leading expert in inorganic solid state chemistry\, with signific ant contributions in synthesis (intermetallics\, Zintl phases\, pnictides \, chalcogenides)\, characterization (X-ray diffraction\, XPS\, physical properties)\, and applications (magnetic\, thermoelectric\, superconducti ng\, optical materials).  In recent years\, he has been at the forefront of applying machine-learning approaches to materials discovery.  He has p ublished >230 articles and given >120 invited presentations.  He has serv ed on the editorial boards of Chemistry of Materials\, Journal of Solid S tate Chemistry\, and Acta Crystallographica.  He has received the Faculty of Science Research Award and many teaching awards at the University of Alberta.  He is vice-chair (2022) and chair-elect (2024) for the Gordon R esearch Conference in Solid State Chemistry. X-ALT-DESC;FMTTYPE=text/html:<html><head><title></title></head><body><p><b >Dr. Arthur Mar</b><br><strong>Department of&nbsp\;Chemistry<br></strong> <strong>University of&nbsp\;Alberta</strong><strong><br></strong></p>\n<p >Abstract:<br>Machine learning algorithms have been applied successfully in many areas of materials chemistry.&nbsp\; An ongoing challenge is make accurate predictions of the crystal structures of inorganic solids\, the ir site preferences\, and their physical properties.&nbsp\; We have previ ously developed machine learning models to predict structures within the large family of intermetallic compounds known as Heusler compounds (used as thermoelectric materials\, ferromagnets\, magnetocaloric materials\, a nd catalysts)\, followed by experimental validation.&nbsp\; Nevertheless\ , skeptics rightfully criticize many of these models as being too “black box\,” with little chemical insight and explainability.&nbsp\; We demonst rate our efforts to generate more interpretable machine learning models\, using the structures of binary rare-earth intermetallics RX as an exampl e\, to illustrate that it is possible to gain insight and practical guida nce to prepare new materials.</p>\n<p>Bio:<br>Dr. Arthur Mar received a P h.D. from Northwestern University in 1992 under the supervision of James A. Ibers.&nbsp\; He worked as an NSERC Postdoctoral Fellow in the laborat ory of Yves Piffard and Jean Rouxel at the Institut des Matériaux de Nant es in 1993–1994.&nbsp\; He is currently a full Professor in the Departmen t of Chemistry at the University of Alberta.&nbsp\; He is a leading exper t in inorganic solid state chemistry\, with significant contributions in synthesis (intermetallics\, Zintl phases\, pnictides\, chalcogenides)\, c haracterization (X-ray diffraction\, XPS\, physical properties)\, and app lications (magnetic\, thermoelectric\, superconducting\, optical material s).&nbsp\; In recent years\, he has been at the forefront of applying mac hine-learning approaches to materials discovery.&nbsp\; He has published &gt\;230 articles and given &gt\;120 invited presentations.&nbsp\; He has served on the editorial boards of Chemistry of Materials\, Journal of So lid State Chemistry\, and Acta Crystallographica.&nbsp\; He has received the Faculty of Science Research Award and many teaching awards at the Uni versity of Alberta.&nbsp\; He is vice-chair (2022) and chair-elect (2024) for the Gordon Research Conference in Solid State Chemistry.</p>\n<p></p ></body></html> DTSTART;TZID=America/Chicago:20220825T160000 DTEND;TZID=America/Chicago:20220825T170000 SEQUENCE:0 URL:https://physics.missouristate.edu/seminars.htm CATEGORIES:Public,Alumni,Current Students,Faculty,Future Students,Staff END:VEVENT END:VCALENDAR