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DTSTART:20070311T020000
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UID:ea0e16db-d22a-4923-81ca-a8f50c45fd99.216215@calendar.missouristate.edu
CREATED:20210407T170031Z
LAST-MODIFIED:20210407T170031Z
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SUMMARY:PAMS Seminar: "Designing Ligands with Predetermined Metal Ion Sele
 ctivity using Electronic Structure Theory\, Machine Learning\, and Molecu
 lar Mechanics" by Dr. Marilu Perez Garcia
DESCRIPTION:Dr. Marilu Perez GarciaCritical Materials InstituteAmes Labora
 tory\n\n\nAbstract:Purifying critical materials needed for high-tech equi
 pment and machinery from raw or recycled sources often requires a solvent
  extraction step. Tailoring a solvent extractant for specific systems cou
 ld save significant time and resources. However\, tailoring extractants f
 or different feed compositions is prohibitively expensive. Focusing on th
 e ligand metal ion binding site\, our team uses machine learning (ML) and
  access to large amounts of empirical data to predict absolute aqueous st
 ability constants for a given ligand. To train the ML algorithm\, we comb
 ine data related to the ligands\, the metal ions\, and data obtained from
  ab initio and molecular mechanics calculations. The prediction software 
 is coupled with HostDesigner to quickly generate and rank theoretical lig
 ands by selectivity between ions of interest. This software\, currently u
 nder development\, will enable rapid down selection of promising targets\
 , guide empirical research\, and help drive innovation in solvent extract
 ion science.\n\n\nThis seminar will be held exclusively on Zoom (955 5209
  1021). Please visit the Physics Seminars page for a link.
X-ALT-DESC;FMTTYPE=text/html:&lt;html&gt;&lt;head&gt;&lt;title&gt;&lt;/title&gt;&lt;/head&gt;&lt;body&gt;&lt;p&gt;&lt;b
 &gt;Dr. Marilu Perez Garcia&lt;br&gt;Critical&amp;nbsp\;&lt;/b&gt;&lt;b&gt;Materials&amp;nbsp\;Institu
 te&lt;br&gt;&lt;/b&gt;&lt;b&gt;Ames Laboratory&lt;/b&gt;&lt;/p&gt;\n&lt;p&gt;Abstract:&lt;br&gt;Purifying critical 
 materials needed for high-tech equipment and machinery from raw or recycl
 ed sources often requires a solvent extraction step. Tailoring a solvent 
 extractant for specific systems could save significant time and resources
 . However\, tailoring extractants for different feed compositions is proh
 ibitively expensive. Focusing on the ligand metal ion binding site\, our 
 team uses machine learning (ML) and access to large amounts of empirical 
 data to predict absolute aqueous stability constants for a given ligand. 
 To train the ML algorithm\, we combine data related to the ligands\, the 
 metal ions\, and data obtained from ab initio and molecular mechanics cal
 culations. The prediction software is coupled with HostDesigner to quickl
 y generate and rank theoretical ligands by selectivity between ions of in
 terest. This software\, currently under development\, will enable rapid d
 own selection of promising targets\, guide empirical research\, and help 
 drive innovation in solvent extraction science.&lt;/p&gt;\n&lt;p&gt;This seminar will
  be held exclusively on Zoom (955 5209 1021). Please visit the&amp;nbsp\;&lt;a h
 ref="https://physics.missouristate.edu/seminars.htm"&gt;Physics Seminars pag
 e&lt;/a&gt; for a link.&lt;/p&gt;&lt;/body&gt;&lt;/html&gt;
DTSTART;TZID=America/Chicago:20210422T160000
DTEND;TZID=America/Chicago:20210422T170000
SEQUENCE:0
URL:https://physics.missouristate.edu/seminars.htm
CATEGORIES:Public,Alumni,Current Students,Faculty,Future Students,Staff
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