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SUMMARY:Learning How to Computationally Design Better Organometallic Cataly
 sts
LOCATION:Chemistry, A101
TZID:America/Denver
DTSTART:20231012T160000
UID:2026-04-05-21-07-17@natsci.colostate.edu
DTSTAMP:20260405T210717
Description:Abstract:\n\nI will describe my group’s use of DFT static and
  dynamics calculations combined with automation tools\, ligand libraries\,
  and machine learning to design new and more selective organometallic cata
 lysts. I will emphasize our strategies and tools to design Cr-based ethyle
 ne oligomerization catalysts and Fe-oxo based alkane C-H functionalization
  catalysts.\n\n&nbsp\;\n\nAbout the Speaker:\n\nDaniel H. Ess is a profess
 or in the Department of Chemistry and Biochemistry at BYU (https://esslab.
 byu.edu/). He received his B.S. from BYU and Ph.D. from UCLA followed by p
 ostdoctoral work at Scripps-Florida\, Caltech\, and UNC Chapel Hill. His g
 roup uses and develops computational chemistry tools to discover reaction 
 mechanisms\, reactivity and selectivity principles\, and design catalysts.
  His research ranges from applied catalyst prediction and realization with
  industrial collaborators to fundamental insights from direct dynamics stu
 dies. He also carries out experimental gas-phase light alkane C-H function
 alization reactions. He directs a summer NSF REU program (https://reu.chem
 .byu.edu/). He is the creator and director of the BYU Chem Camp program (h
 ttps://chemcamp.byu.edu/). For education\, he creates interactive learning
  tools\, such as the “iORA” application that displays reactive organic
  trajectories and is available on the iPhone App Store. 4:00 pm
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