Graduate Student Seminar
October 13, 2023
12:00 p.m. ET
Scaife Hall 105
Making Artificial Intelligence Work in Materials Research
The use of modern artificial intelligence, machine learning, informatics, and other data mining approaches is a relatively new development in the chemical and materials domain. These techniques have been exceedingly successful in other application fields, and since there is no fundamental reason why they should not have a similarly transformative impact on chemical and materials research, there is now a concerted effort by the community to introduce them in this new context. These techniques hold tremendous promise for the practical realization of accelerated discovery, rational design, and inverse development of next generation materials systems. However, adapting techniques from other application domains for the study of chemical and materials systems requires a substantial rethinking and redevelopment of the existing methods. In this presentation, we will discuss our efforts along these lines.
Johannes Hachmann, University of Buffalo/New York State Center of Excellence in Materials Informatics
Hachmann is an Associate Professor of Chemical Engineering at the University at Buffalo (UB), the Director of the Engineering Science in Data Science graduate program, a Core Member of the UB Computational and Data-Enabled Science and Engineering graduate program, and a Faculty Member of the New York State Center of Excellence in Materials Informatics. He earned a Dipl.-Chem. degree (2004) after undergraduate studies at the universities of Jena and Cambridge, M.Sc. (2007) and Ph.D. (2010) degrees in Chemistry from Cornell University, and he conducted postdoctoral research at Harvard University before joining the UB faculty in 2014. The research of the Hachmann Group fuses (first-principles) molecular and materials modeling with virtual high-throughput screening and modern data science (i.e., the use of database technology, machine learning, artificial intelligence, and informatics) to advance a data-driven discovery and rational/inverse design paradigm in the chemical and materials disciplines. One of the centerpieces of the group’s efforts is the creation of an open, general-purpose software ecosystem for the data-driven design of chemical systems and the exploration of chemical space. This work was recognized with a 2018 NSF CAREER Award.
Upcoming Events
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September 20 2024
12:45 PM ET
Materials Science and Engineering
Can Unknown Materials Properties Be Accurately Predicted?, presented by Zi-Kui Liu, Penn State University
Wean Hall 7500
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September 23 2024
1:00 PM ET
Materials Science and Engineering
Combat Hot Cracking in Additive Manufacturing, presented by Guannan Tang
2327 Wean Hall, Mehl Room
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September 26 2024
9:30 AM ET
Materials Science and Engineering
Shear-induced Fiber Alignment in Synthetic and Biological Systems to Enhance 3D Anisotropic Properties Using FRESH 3D Printing, presented by Neeha Dev Arun
6142 Scott Hall
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September 27 2024
12:45 PM ET
Materials Science and Engineering
"Unveiling the Future of Color Matching: PPG Digitization, Color Physics, and AI insights," presented by Chris Childs, PPG
Wean Hall 7500
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October 4 2024
12:45 PM ET
Materials Science and Engineering
"Deconstructing Meritocracy: Unpacking Attacks on Diversity, Equity, and Inclusion," presented by John Herbert, Ohio State University
Wean Hall 7500
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October 11 2024
12:45 PM ET
Materials Science and Engineering
Seminar presented by Zakaria Al Balushi, University of California, Berkley
Wean Hall 7500