Multi-Timescale Accelerated Dynamics and Trajectory Analysis of Alloy Surface Transformations Using Novel Interatomic Potentials

 

Chair:

Dr. Judith Yang, Chemical and Petroleum Engineering Department, Physics and Astronomy Department, University of Pittsburgh

  

Committee: 

Dr. Karl Johnson, Chemical and Petroleum Engineering Department, University of Pittsburgh

 

Dr. John Keith, Department of Chemical and Petroleum Engineering, University of Pittsburgh

 

Abstract:

Although the equilibrium composition of many alloy surfaces is understood, the rate of transient surface segregation during annealing or oxidation is not known despite their crucial effects on alloy corrosion and catalytic reactions which occur on overlapping timescales. This work focuses on computational studies of the surface segregation of CuNi alloys, with methods and analyses generally applicable to all other bimetals. Three accelerated methods are utilized to transiently evolve the system towards equilibrium: parallel trajectory splicing (ParSplice), adaptive kinetic Monte Carlo (AKMC), and kinetic Monte Carlo (KMC) from cluster expansion. From nanosecond to second timescales, this hierarchy of multiscale approaches can observe stochastic events not typically seen with standard MD, closing the gap between computational and experimental timescales for surface segregation and providing a timescale for vacuum segregation to occur. Segregation equilibrates at hundreds of ms in the first layer and multiple seconds in the interior (third) layer. First-principles results are also presented for the CuNi(100) surface facet primarily in vacuum, with new interatomic potentials introduced to model interactions with oxygen optimized via evolutionary algorithm and deep learning based upon this DFT ground-truth dataset. These data and resulting models reveal the cutoff for oxygen surface coverage beyond which competing oxides can form atop the bimetal as well as a preference for NiO formation, reversing the vacuum segregation trend previously observed.

 

Event Details

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Zoom Meeting Link: https://pitt.zoom.us/j/97216807456

Meeting ID: 972 1680 7456

Passcode: 060775

 

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