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<b><i><span style="margin:0px">"Exploring the Compositional Space of Materials via Classical and Quantum Computers"</span><br>
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<div><b><span style="margin:0px"></span>Houlong Zhuang</b></div>
<div>School for Engineering of Matter, Transport and Energy,</div>
<div>Arizona State University</div>
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<div>NOTE: <i>Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic. All are welcome to attend.
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<div><b>Meeting # 942 7359 5552 </b></div>
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<div>Schedule: </div>
<div><b>Teatime</b> - Virtual (via Zoom) </div>
<div>* <b>3:00 to 3:30 PM</b> Eastern Time (US and Canada)</div>
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<div><b>Colloquium</b> - F2F (in 499 DSL) | Virtual (via Zoom) </div>
<div>* <b>3:30 to 4:30 PM</b> Eastern Time (US and Canada) </div>
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<div><b>Abstract: </b></div>
<div>Computational materials design often starts with selecting elements and determining their concentrations. This process is tantamount to exploring the vast compositional space, which is a daunting task if it is performed by the traditional trial-and-error
manner. In this talk, I will use multi-principal component alloys (also known as high-entropy alloys) as an example to show our recent results of using classical and quantum computers to explore the compositional space of these alloys. Specifically, my group
trained a classical deep neural network to achieve the goal that given a certain combination of elements and concentrations, the trained model is capable of efficiently predicting the resulting atomic arrangement. I will also show our recent efforts of using
quantum computers to accomplish the same goal.</div>
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