This workshop will help participants to understand the impact of students' prior knowledge on their learning, using current pedagogical research and frameworks. Attendees will spend time building an individualized toolkit for using both low-tech and high-tech classroom techniques for assessing prior knowledge.  Facilitators: Kyler Schubkegel, Graduate Associate, Kaneb Center for Teaching ExcellenceGelei Xu, Graduate Associate, Kaneb Center for Teaching Excellence

Looking for a group to crochet or knit with? All are welcome to join this group of needlework enthusiasts. -- https://sjcpl.libnet.info/event/14171070

Are you learning Irish on campus or would like to learn some Irish phrases? Join us in KNI's office suite, 3130 Jenkins Nanovic Halls for tea, biscuits, and conversation practice in Irish. Originally published at irishstudies.nd.edu.

https://fiddlershearth.com/

Visio Divina, or “sacred seeing,” is an ancient form of Christian prayer in which we allow our hearts and imaginations to prayerfully and meditatively enter into an image. Join the Raclin Murphy Museum of Art for guided sessions of visio divina with sacred art in the Mary, Queen of Families Chapel. This opportunity is a wonderful way to spiritually engage with this unique, art-filled space. Whether you are new to the practice of visio divina or a long-time devotee, all are welcome for a moment of peace, prayer, and meditation. Meet at the Welcome Desk on Level 1. Always free and open to all. Originally published at raclinmurphymuseum.nd.edu.

Andrew J. Medford Associate Professor in the School of Chemical & Biomolecular Engineering at Georgia Institute of Technology As a faculty member, his group’s research lies at the intersection of catalysis and surface science, computational chemistry, and machine learning, and he has received several research awards, including the NSF CAREER Award and the Early Career Award from the ACS CATL division.2025 THIELE LECTURESHIP AWARDEESeminar Title: Machine learning in computational catalysis: from electronic structure theory to kinetic models Abstract: Heterogeneous catalysis is an inherently multi-scale process that ultimately connects the behavior of electrons to the global-scale production of chemicals. Understanding how these processes interact is a never-ending challenge, but recent research has shown that application of machine learning and artificial intelligence models is a promising strategy for discovery of novel catalytic materials and advancing fundamental insight at the interface between chemistry and physics. This talk will present progress in the application of machine learning from opposite ends of the multi-scale spectrum. At the scale of electrons, the talk will introduce the use of machine learning approaches to establish a new paradigm of exchange-correlation functional design that uses "multipole features" to provide flexibility between the solid-state and molecular electronic environments that arise in solid-gas/liquid interfaces of heterogeneous catalysis. At the scale of reactors, the use of "kinetics informed neural networks" will be presented as a route to directly analyze large volumes of transient kinetic and spectroscopic data to extract rate parameters that can help elucidate intrinsic kinetics and reaction mechanisms. The talk will demonstrate how these fundamentally different approaches have complementary strengths and weaknesses, indicating that a combination of methods will ultimately be required to understand the complex multi-scale processes involved in heterogeneous catalysis. Chemical and Biomolecular Engineering: Thiele Lecture Series