1. Autofit and the Spectrum of Eugenol
New College of Florida: Erika Riffe, Sawyer Welden, Emma Cockram,
Katherine Ervin, Steven Shipman
Coker College: Cameron M. Funderburk, Gordon G. Brown
Emory University: Susanna L. Widicus Weaver

2. Cloves and Clove Oil (Eugenol)
Image from
Images from

3. Eugenol Structures and Energies (B3LYP/6-311+G(d,p))
0 cm-1
130 cm-1
343 cm-1
A =
B = 617.3
C = 445.2
A =
B = 524.5
C = 414.3
A =
B = 480.3
C = 410.3
Methoxy Scan (from minimum)
Alkene Scan (from minimum)

4. Coker College Spectrometer

5. Eugenol Spectrum Sample T = 80 °C, Expansion T = 5 K
20 μs FID, 10k averages, S/N: 53:1
106 peaks with S/N > 3:1

6. Autofit

7. Previous Autofit Results

8. Cluster Computing Main Challenges:
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Main Challenges:
Need to port code to very different environment
No interactive scripts – submit job and log out
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9. Non-interactive Scripts
Most parameters read in from input file or command line.
Most significant: automated selection of fitting / scoring transitions
Process
Simulate based on type (a-type, b-type, etc.) and initial constants
Find 10 most-intense peaks from .cat file in frequency range
Choose 3 fit transitions based on weighted combination of:
linear independence
sensitivity to rotational constants
overall intensity
Remaining 7 are scoring transitions

10. Grid-Based Search A = 1781, B = 721, C = 514
505 – 404: 5730
533 – 432: 6248
514 – 413: 6548
505 – 404: 7372
533 – 432: 7393
514 – 413: 7476
Transitions move dramatically with rotational constants.
Use larger search windows or grid of initial guesses (or both).
Sampling volume defined by A ≥ B ≥ C. What’s the most efficient method, given transition sensitivity? Equal spacing for now. A A B B C C

11. Tests with Hexanal Dominant conformers identified as expected.
For blind search: many autofit jobs!
Wrote scripts to summarize output from hundreds of jobs.
# jobs = [(1/2) (n3 + n2)] x 6 (a-, b-, c-, ab-, ac-, bc-type spectra)

12. Summary
Eugenol extracted via steam distillation in high yield and purified.
Cluster-ready autofit with grid-based search works.
Eugenol autofit jobs are on-going (perhaps already completed!)
Future Work
Revamp code using Open MPI to run on multiple compute nodes (currently limited to one). Easy routes for further parallelization.
Consider automatically classifying spectrum by type to reduce number of jobs – machine learning?
Bulk of run time is spent on SPCAT and SPFIT – possible to optimize?
Investigate alternate optimization methods (swarm algorithms)

13. Acknowledgements
Anne-Lise Emig, Oliver Gray, Wes Harper, Maggie Swerdloff
Luyao Zhou, Cherry L. Emerson Center for Scientific Computation

15. Eugenol Spectrum Sample T = 80 °C Expansion T = 5 K
20 μs FID, 10k averages
S/N: 53:1
220 peaks with S/N > 3:1