清華大學材料科學與工程研究院《材料科學論壇》學術報告

報告時間：2024年10月15日上午10:00

報告人：Taylor D. Sparks

報告地點：清華大學逸夫技術科學樓A205學術報告廳

邀請人：沈洋老師

報告題目：How machine learning is changing the way we predict new crystal structures

報告簡介：

Crystal structure prediction has long fascinated scientists. There has been intense investigation over the last century ranging from simplistic rules to data-driven predictions and, most recently, generative artificial intelligence tools developed by academics and now deployed at scale by private companies like DeepMind. In this talk, I describe the timeline of crystal structure prediction and describe how machine learning has supplemented and, in some cases, replaced traditional approaches. I will compare generative models including variational autoencoders, generative adversarial networks, and diffusion models and describe new efforts to condition these models to achieve inverse design of new crystal structures. I’ll give specific examples of our xtal2png and CrysTens representations and our machine learning contributions to greatly accelerate the Flexible Unit Structure Engine (FUSE) software package.

報告人簡介：

Dr. Sparks is an Professor of Materials Science and Engineering at the University of Utah and recently completed a sabbatical at the University of Liverpool with support from the Royal Society Wolfson Visiting Fellow program. He holds a BS in MSE from the UofU, MS in Materials from UCSB, and PhD in Applied Physics from Harvard University. He was a recipient of the NSF CAREER Award and a speaker for TEDxSaltLakeCity. He is active in MRS, TMS, and ACERS societies and has served as an Associate Editor for the journals Computational Materials Science and Data in Brief. He is the Editor-in-Chief elect for the Integrating Materials and Manufacturing Innovation When he’s not in the lab you can find him running his podcast “Materialism,” creating materials educational content for his YouTube channel, or canyoneering with his 4 kids in southern Utah.