Machine learning (ML), molecular dynamics simulations (MD), and high-resolution transmission electron microscopy (HRTEM) are combined to fully understand phase transformations in a model transition metal dichalcogenide (TMD) layer (MoS2).
Significance and Impact
A defect distribution is predicted as the driver of the semiconducting to metallic phase transformation in MoS2, and validate the prediction with in situ HRTEM experiments.
- Supervised ML performs structural optimization, identifying the change from a less stable point defect to a more stable extended structure; genetic algorithms with MD identify stable structures. The CNM Carbon cluster performed a portion of the ML calculations and atomistic simulations.
Work was performed in part at the Center for Nanoscale Materials.
About Argonne’s Center for Nanoscale Materials
The Center for Nanoscale Materials is one of the five DOE Nanoscale Science Research Centers, premier national user facilities for interdisciplinary research at the nanoscale supported by the DOE Office of Science. Together the NSRCs comprise a suite of complementary facilities that provide researchers with state-of-the-art capabilities to fabricate, process, characterize and model nanoscale materials, and constitute the largest infrastructure investment of the National Nanotechnology Initiative. The NSRCs are located at DOE’s Argonne, Brookhaven, Lawrence Berkeley, Oak Ridge, Sandia and Los Alamos National Laboratories. For more information about the DOE NSRCs, please visit https://science.energy.gov/bes/suf/user-facilities/nanoscale-science-research-centers/.
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