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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January 23 2026
10:00 AM ET
Materials Science and Engineering
Retractions: On The Rise, But Not Enough, presented by Ivan Oransky, Retraction Watch
CUC McConomy Auditorium
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January 30 2026
10:00 AM ET
Materials Science and Engineering
Fountain of Youth: metals that resist fatigue and heal themselves, presented by Brad Boyce, Sandia National Laboratories
CUC McConomy Auditorium
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February 4 2026
8:30 AM - 2:00 PM ET
Rivers Casino Event Center
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February 6 2026
10:00 AM ET
Materials Science and Engineering
presented by Kaushik Bhattacharya, Caltech
CUC McConomy Auditorium
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February 13 2026
10:00 AM ET
Materials Science and Engineering
Interfacial Fracture in Soft Polymer Networks: Revisiting Gent's Picture, presented by Gabriel Sanoja Lopez, University of Texas, Austin
CUC McConomy Auditorium
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February 20 2026
10:00 AM ET
Materials Science and Engineering
High-Fidelity Atomistic Simulations of Chemistry-Microstructure Interactions in Metals, presented by Rodrigo Freitas, Massachusetts Institute of Technology
CUC McConomy Auditorium