TU1.R1.1
Keynote
USING MOLECULAR MODELING AND MACHINE LEARNING TO ADDRESS STABILITY CHALLENGES FOR ZEOLITE CATALYSTS
Chris Paolucci, University of Virginia
Session:
TU1.R1: Computational Chemistry and Catalysis, Data Science, ML I
Track:
Computational Chemistry and Catalysis, Data Science, and AI/ML in Reaction Engineering
Location:
Galleria I & II
Presentation Time:
Tue, 18 Feb, 10:45 - 11:05 CT (UTC -6)
Presentation
Discussion
Resources
No resources available.
Session TU1.R1
TU1.R1.1: USING MOLECULAR MODELING AND MACHINE LEARNING TO ADDRESS STABILITY CHALLENGES FOR ZEOLITE CATALYSTS
Chris Paolucci, University of Virginia
TU1.R1.2: Application of surrogate modelling to accelerate design space exploration for catalytic reactor systems
Stepan Spatenka, Sreekumar Maroor, Siemens Industry Software Ltd., United Kingdom; Udit Gupta, Siemens Industry Software Inc., United States
TU1.R1.3: Advantages in the use of AI-based regressions for the kinetic modelling of industrial catalysts
Hugo Petremand, Andrea Pappagallo, Paul Scherrer Institute, Switzerland; EMANUELE MOIOLI, Politecnico di Milano, Italy
Contacts