A web-based GUI for INTERSECT has been created which allows a user to configure an experiment on an electron microscope, setting such parameters as maximum number of steps for the machine learning algorithm to perform.
Researchers from 91°µÍø (ORNL) used high-throughput computational techniques to identify a new class of 2D nanomaterial, MXenes including boron-nitride.
Researchers from University of California Riverside, Drexel, and 91°µÍø (ORNL) identified the atomistic mechanism by which MXenes degrade in water.
A graph convolutional neural network (GCNN) was trained to accurately predict formation energy and mechanical properties of solid solution alloys crystallized in different lattice structures, thereby advancing the design of alloys for improving mechanic
Automated experiments in Scanning Transmission Electron Microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials.
Using first-principles calculations and group-theory-based models, we study the stabilization of ferrielectricity (FiE) in CuInP2Se6.