BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Denver
X-LIC-LOCATION:America/Denver
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260422T000712Z
LOCATION:501-502
DTSTART;TZID=America/Denver:20231113T144000
DTEND;TZID=America/Denver:20231113T150000
UID:submissions.supercomputing.org_SC23_sess440_ws_ai4s113@linklings.com
SUMMARY:Machine Learning Applied to Single-Molecule Activity Prediction
DESCRIPTION:Kendric Hood and Qiang Guan (Kent State University)\n\nCatalyt
 ic processes are used in about 1/3 of US manufacturing, from the field of 
 chemical engineering to renewable energy. Assessing the activity of single
 -molecules, or individual molecules, is necessary to the development of ef
 ficient catalysts. Their heterogeneity structure leads to particle-specifi
 c catalytic activity. Experimentation with single-molecules can be time co
 nsuming and difficult. We purpose a Machine learning (ML) model that allow
 s chemical researchers to run shorter single-molecule experiments to obtai
 n the same level of results. We use common and widely understood ML method
 s to reduce complexity and enable accessibility to the chemical engineerin
 g community. We reduce the experiment time by up to 83%. Our evaluation sh
 ows that a small data set is sufficient to train an acceptable model. 300 
 experiments are needed, including the validation set. We use a well unders
 tood multi-layer perceptron (MLP) model. We show that more complex models 
 are not necessary, and simpler methods are not sufficient.\n\nTag: Artific
 ial Intelligence/Machine Learning\n\nRegistration Category: Workshop Reg P
 ass\n\nSession Chairs: Murali Emani (Argonne National Laboratory (ANL)); G
 okcen Kestor (Barcelona Supercomputing Center (BSC); University of Califor
 nia, Merced); and Dong Li (University of California, Merced)\n\n
END:VEVENT
END:VCALENDAR