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Title: Reconstruction of effective potential from statistical analysis of dynamic trajectories

Abstract

The broad incorporation of microscopic methods is yielding a wealth of information on the atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in open volumes. Analysis of such data necessitates statistical frameworks to convert observed dynamic behaviors to effective properties of materials. Here, we develop a method for the stochastic reconstruction of effective local potentials solely from observed structural data collected from molecular dynamics simulations (i.e., data analogous to those obtained via atomically resolved microscopies). Using the silicon vacancy defect in graphene as a model, we apply the statistical framework presented herein to reconstruct the free energy landscape from the calculated atomic displacements. Evidence of consistency between the reconstructed local potential and the trajectory data from which it was produced is presented, along with a quantitative assessment of the uncertainty in the inferred parameters.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2];  [3]; ORCiD logo [2]; ORCiD logo [4]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2];  [5]
+ Show Author Affiliations
  1. Univ. of Tennessee, Knoxville, TN (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Florida State Univ., Tallahassee, FL (United States)
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Manchester (United Kingdom)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); Engineering and Physical Sciences Resource Council (EPSRC); National Science Foundation (NSF)
OSTI Identifier:
1869131
Alternate Identifier(s):
OSTI ID: 1635058
Grant/Contract Number:  
AC05-00OR22725; EP/N510129/1; DMS-1720222
Resource Type:
Accepted Manuscript
Journal Name:
AIP Advances
Additional Journal Information:
Journal Volume: 10; Journal Issue: 6; Journal ID: ISSN 2158-3226
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS; Brownian motion; molecular dynamics; computational physics; stochastic processes; numerical methods; free energy landscapes; graphene

Citation Formats

Yousefzadi Nobakht, Ali, Dyck, Ondrei, Lingerfelt, David B., Bao, Feng, Ziatdinov, Maxim, Maksov, Artem B., Sumpter, Bobby G., Archibald, Richard, Jesse, Stephen, Kalinin, Sergei V., and Law, Kody H. Reconstruction of effective potential from statistical analysis of dynamic trajectories. United States: N. p., 2020. Web. doi:10.1063/5.0006103.
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Yousefzadi Nobakht, Ali, Dyck, Ondrei, Lingerfelt, David B., Bao, Feng, Ziatdinov, Maxim, Maksov, Artem B., Sumpter, Bobby G., Archibald, Richard, Jesse, Stephen, Kalinin, Sergei V., & Law, Kody H. Reconstruction of effective potential from statistical analysis of dynamic trajectories. United States. https://doi.org/10.1063/5.0006103
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Yousefzadi Nobakht, Ali, Dyck, Ondrei, Lingerfelt, David B., Bao, Feng, Ziatdinov, Maxim, Maksov, Artem B., Sumpter, Bobby G., Archibald, Richard, Jesse, Stephen, Kalinin, Sergei V., and Law, Kody H. Thu . "Reconstruction of effective potential from statistical analysis of dynamic trajectories". United States. https://doi.org/10.1063/5.0006103. https://www.osti.gov/servlets/purl/1869131.
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@article{osti_1869131,
title = {Reconstruction of effective potential from statistical analysis of dynamic trajectories},
author = {Yousefzadi Nobakht, Ali and Dyck, Ondrei and Lingerfelt, David B. and Bao, Feng and Ziatdinov, Maxim and Maksov, Artem B. and Sumpter, Bobby G. and Archibald, Richard and Jesse, Stephen and Kalinin, Sergei V. and Law, Kody H.},
abstractNote = {The broad incorporation of microscopic methods is yielding a wealth of information on the atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in open volumes. Analysis of such data necessitates statistical frameworks to convert observed dynamic behaviors to effective properties of materials. Here, we develop a method for the stochastic reconstruction of effective local potentials solely from observed structural data collected from molecular dynamics simulations (i.e., data analogous to those obtained via atomically resolved microscopies). Using the silicon vacancy defect in graphene as a model, we apply the statistical framework presented herein to reconstruct the free energy landscape from the calculated atomic displacements. Evidence of consistency between the reconstructed local potential and the trajectory data from which it was produced is presented, along with a quantitative assessment of the uncertainty in the inferred parameters.},
doi = {10.1063/5.0006103},
journal = {AIP Advances},
number = 6,
volume = 10,
place = {United States},
year = {Thu Jun 25 00:00:00 EDT 2020},
month = {Thu Jun 25 00:00:00 EDT 2020}
}
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https://doi.org/10.1063/5.0006103
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