journal article·2020-06-01Open access

Reconstruction of effective potential from statistical analysis of dynamic trajectories

A. Yousefzadi Nobakht, O. Dyck, D. B. Lingerfelt, F. Bao, M. Ziatdinov, A. Maksov, B. G. Sumpter, R. Archibald, S. Jesse, S. V. Kalinin, K. J. H. Law

AIP Advances· Volume 10· Issue 6

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.
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Research context

OpenAlex topics: Force Microscopy Techniques and Applications · Advanced Electron Microscopy Techniques and Applications · Machine Learning in Materials Science

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical, Aerospace, and Biomedical Engineering, The University of Tennessee 1 , Knoxville, Tennessee 37996, USA

The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory 2 , Oak Ridge, Tennessee 37831, USA

Department of Mathematics, Florida State University 3 , Tallahassee, Florida 32304, USA

Bredesen Center for Interdisciplinary Research and Education, The University of Tennessee 4 , Knoxville, Tennessee 37996, USA

Computational Sciences & Engineering Division, Oak Ridge National Laboratory 5 , Oak Ridge, Tennessee 37831, USA

Computer Science and Mathematics Division, Oak Ridge National Laboratory 6 , Oak Ridge, Tennessee 3783, USA

School of Mathematics, University of Manchester 7 , Manchester M13 9PL, United Kingdom

Funding

U.S. Department of Energy · Directorate for Mathematical and Physical Sciences · Engineering and Physical Sciences Research Council · Alan Turing Institute

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