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.
| Original language | English |
|---|---|
| Article number | 065034 |
| Journal | AIP Advances |
| Volume | 10 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 1 2020 |
Funding
A.Y.N. would like to thank Dr. Seungha Shin and Jiaqi Wang for their expertise and assistance at Nano-Heat lab at the University of Tennessee. This work was also supported by The Alan Turing Institute under the EPSRC Grant No. EP/N510129/1 (K.J.H.L.). This study is based on the work supported by the U.S. Department of Energy, Office of Science, Division of Materials Science and Engineering, Basic Energy Sciences (S.V.K., O.D., and S.J.) and was performed at and partially supported by (M.Z., D.B.L., and B.G.S.) the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), which is a U.S. Department of Energy, Office of Science User Facility. R.A. and F.B. would like to acknowledge the support by the Scientific Discovery through Advanced Computing (SciDAC) funded by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research through FASTMath Institutes and (F.B.) partial support by the U.S. National Science Foundation under Contract No. DMS-1720222.
| Funders | Funder number |
|---|---|
| CNMS | |
| Division of Materials Science and Engineering | |
| FASTMath Institutes | |
| Oak Ridge National Laboratory | |
| U.S. National Science Foundation | |
| U.S. Department of Energy | |
| Directorate for Mathematical and Physical Sciences | 1720222 |
| Office of Science | |
| Basic Energy Sciences | |
| Alan Turing Institute | |
| Engineering and Physical Sciences Research Council |