6 research outputs found

    A Framework to Learn Physics from Atomically Resolved Images

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    Reconstruction of effective potential from statistical analysis of dynamic trajectories

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    The broad incorporation of microscopic methods is yielding a wealth of information on 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 stochastic reconstruction of effective acting potentials from observed trajectories. Using the Silicon vacancy defect in graphene as a model, we develop a statistical framework to reconstruct the free energy landscape from calculated atomic displacements.Comment: 12 pages, 5 figures. This manuscript is a part of update to previous work (arXiv:1804.03729v1) authors found some of the analysis in the previous work to be not accurate and this manuscript is a partial update to that wor

    Emerging materials intelligence ecosystems propelled by machine learning

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