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

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)065034
JournalAIP Advances
Issue number6
Early online date25 Jun 2020
Publication statusE-pub ahead of print - 25 Jun 2020


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