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Final Doctoral Defense - Nathan Walter, Ph.D. Candidate

Event Type
Seminar/Symposium
Sponsor
Department of Nuclear, Plasma, and Radiological Engineering
Location
2169 Beckman Institute
Date
Jul 22, 2019   2:00 pm  
Speaker
Nathan Walter, PhD Candidate
Cost
Free and Open to the Public
E-Mail
gwitmer2@illinois.edu
Phone
217-333-2295
Views
14
Originating Calendar
NPRE Events

Understanding Long Timescale Phenomena in Atomic Systems from Advanced Sampling Molecular Methods

 

Abstract: Conventional molecular dynamics simulations have proven instrumental to the understanding of material behaviors. However, the temporal constraints of molecular dynamics simulations have limited attempts to capture long timescale dynamics of material systems, such as phase transitions or dynamics of supercooled liquids. This class of phenomena often requires the crossing by thermal activation of large energy barriers, which represent transition states separating two metastable states of a system. Therefore, we propose two methods to simulate the escape of a system from a metastable state to a transition state, which, when repeated, leads to atomic trajectories of extremely slow or rare phenomena of non-equilibrium matter. The first method, all-atom metadynamics, is a version of the popular advanced sampling method metadynamics. By biasing over all atoms rather than collective variables, all-atom metadynamics samples the unbiased potential energy landscape. All-atom metadynamics utility is displayed by studying the nucleation and crystal growth of a model system, while also displaying the computational downside of metadynamics, the scaling over simulation time. Thus, the second method, AscentDynamics, is introduced as an alternative method with constant computational scaling over simulation time. AscentDynamics, based on former surface walking methods, is first verified and validated on simple systems, followed by application of the method to a model liquid system. The method is shown to accurately sample the potential energy landscape without the large computational overhead.

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