BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Missouri State University/Calendar of Events//EN CALSCALE:GREGORIAN X-WR-TIMEZONE:America/Chicago BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:e1256792-7c79-42f7-9c41-1591a7bc6cc8.210872@calendar.missouristate.edu CREATED:20201016T181533Z LAST-MODIFIED:20201016T181533Z LOCATION: SUMMARY:PAMS Seminar: Dr. Dilpuneet Aidhy\, "Properties of Concentrated Al loys Predicted from Atomistic Calculations and Machine Learning" DESCRIPTION:Dr. Aidhy is an assistant professor in the department of mecha nical engineering at the University of Wyoming. His areas of expertise ar e atomistic modeling of materials using density functional theory\, molec ular dynamics simulations and data-science methods.\n\n\nAbstract:\n\n\nC oncentrated alloys\, including high entropy alloys (HEAs)\, consist of mu ltiple principal elements that are randomly distributed on a crystal latt ice. The random distribution of elements leads to a varying energy landsc ape at each atomic site. Consequently\, large variations in various types of defect energies including point defects and stacking faults is common ly observed in HEAs. Statistically capturing the variation requires perfo rming large number of density functional theory calculations. The challen ge is compounded due to the exponentially large number of compositions th at are possible in HEAs. We solve the problem by leveraging machine learn ing tools where the defect energies computed from binary alloys are used to train the models to predict energies in multi-element alloys. We demon strate accurate predictions of defect energies in Ni-based HEAs. A major benefit of this approach is that once the binary database is built and th e model is trained\, defect energies can be easily predicted thereby bypa ssing the need to perform large number of calculations every time a new c omposition is discovered.\n\n\n\n\n\nThis seminar will be held exclusivel y on Zoom (955 5209 1021). Please visit the Physics Seminars page for a l ink. X-ALT-DESC;FMTTYPE=text/html:<html><head><title></title></head><body><p>Dr . Aidhy is an assistant professor in the department of mechanical enginee ring at the University of Wyoming. His areas of expertise are atomistic m odeling of materials using density functional theory\, molecular dynamics simulations and data-science methods.</p>\n<p>Abstract:</p>\n<p>Concentr ated alloys\, including high entropy alloys (HEAs)\, consist of multiple principal elements that are randomly distributed on a crystal lattice. Th e random distribution of elements leads to a varying energy landscape at each atomic site. Consequently\, large variations in various types of def ect energies including point defects and stacking faults is commonly obse rved in HEAs. Statistically capturing the variation requires performing l arge number of density functional theory calculations. The challenge is c ompounded due to the exponentially large number of compositions that are possible in HEAs. We solve the problem by leveraging machine learning too ls where the defect energies computed from binary alloys are used to trai n the models to predict energies in multi-element alloys. We demonstrate accurate predictions of defect energies in Ni-based HEAs. A major benefit of this approach is that once the binary database is built and the model is trained\, defect energies can be easily predicted thereby bypassing t he need to perform large number of calculations every time a new composit ion is discovered.</p>\n<p></p>\n<p>This seminar will be held exclusively on Zoom (955 5209 1021). Please visit the&nbsp\;<a href="https://physics .missouristate.edu/seminars.htm">Physics Seminars page</a> for a link.</p ></body></html> DTSTART;TZID=America/Chicago:20201022T160000 DTEND;TZID=America/Chicago:20201022T170000 SEQUENCE:0 URL:https://physics.missouristate.edu/seminars.htm CATEGORIES:Public,Alumni,Current Students,Faculty,Future Students,Staff END:VEVENT END:VCALENDAR