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SUMMARY:Exploration of energy surfaces and conformation of molecules and s
 olids by utilizing a machine-learning technique
DTSTART;VALUE=DATE-TIME:20191108T054000Z
DTEND;VALUE=DATE-TIME:20191108T060000Z
DTSTAMP;VALUE=DATE-TIME:20260724T113239Z
UID:indico-contribution-35@sshep.snu.ac.kr
DESCRIPTION:Speakers: Jaejun Yu (Seoul National University)\nPredicting th
 e physical properties of novel materials requires an accurate description 
 of atomic interactions as provided by first-principles quantum mechanical 
 calculations. Efficient and practical calculation tools have been develope
 d along with the progress of density functional theory (DFT). Still\, howe
 ver\, the computational complexity associated with the quantum mechanical 
 treatment limits their applications to systems of a few hundreds of atoms 
 at most. Here\, we present an application of the Gaussian process regressi
 on (GPR) scheme to the global optimization\, conformation space annealing\
 , and pathway optimization methods. We demonstrate that the use of GPR-bas
 ed pathway optimization technique\, e.g.\, action-derived molecular dynami
 cs (ADMD) method\, can be useful in enhancing the computational performanc
 e. We will discuss possible future applications of the GPR-based machine l
 earning technique for the exploration of energy surfaces and conformation 
 of molecules and solids.\n\nhttps://sshep.snu.ac.kr/event/107/contribution
 s/35/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/35/
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