<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
</head>
<body>
<div><b><i>"Computational Approaches to Theory and Experiment in Chemical Catalysis"</i></b>
<div><br>
</div>
<div><span style="font-size: 16pt;"><b>Qin Wu</b></span></div>
<div><b>Center for Functional Nanomaterials,</b></div>
<div><b>Brookhaven National Laboratory</b></div>
<div><br>
</div>
<div>NOTE: Please feel <b>free to forward/share this invitation with other groups/disciplines</b> that might be interested in this talk/topic.
<u>All are welcome to attend. </u></div>
<div><br>
</div>
<div><b>https://fsu.zoom.us/j/94273595552 </b></div>
<div>Meeting # <b>942 7359 5552 </b></div>
<div><br>
</div>
<div><b>Mar 9, 2022</b>, Schedule:</div>
<div>[ all times are Eastern Time (US and Canada) ]</div>
<div>* 3:00 to 3:30 PM - Teatime (via Zoom) </div>
<div>* <b>3:30 to 4:30 PM - Colloquium</b> - Attend F2F (in 499 DSL) or Virtually (via Zoom)
</div>
<div> </div>
<div><br>
</div>
<div><b>Abstract: </b></div>
</div>
<div>It has been a long-term goal for nanoscience research to integrate experiment, theory, and computational approaches. While predictive models from theory and computation are still rare, the goal of integration is becoming closer because of the rapid progress
in artificial intelligence and machine learning. Using two examples from our current work in catalysis, I will discuss how computation is used together with experiment and theory. In the first example, a data-driven machine learning approach is proposed, coupled
with dynamic experiments, to do inverse chemical kinetics modeling. In the second example, a descriptor based on theoretical insight and ab initio calculations is introduced for the screening of semiconducting catalytic systems.<br>
</div>
<div style="font-family: Tahoma, Geneva, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style="font-family: Tahoma, Geneva, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
</body>
</html>