Ayana Ghosh Research Scientist Contact ghosha@ornl.gov All Publications Direct Fabrication of Atomically Defined Pores in MXenes Using Feedback-Driven STEM Structural mode coupling in perovskite oxides using hypothesis-driven active learning Designing workflows for materials characterization Towards physics-informed explainable machine learning and causal models for materials research Machine learning for automated experimentation in scanning transmission electron microscopy... Predictive Design of Hybrid Improper Ferroelectric Double Perovskite Oxides Identification of novel organic polar materials: A machine learning study with importance sampling Stress and Curvature Effects in Layered 2D Ferroelectric CuInP2S6 Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space Design of high polarization low switching barrier hybrid improper ferroelectric perovskite oxide superlattices Switching of Hybrid Improper Ferroelectricity in Oxide Double Perovskites Fabrication of Atomic-scale Defect Structures within 2D Materials through Automated Electron Beam Control Probe microscopy is all you need AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy... A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscop... First-Principles Study of Ferroelectric and Optical Properties in Derivatives of Thiourea... Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations... Building Atomic and Plasmonic Devices via Electron Beams: from Desired Structures to Desired Properties Atomic-scale Fabrication of 1D-2D Nano Hetero-structures within 2D Materials through Automated Tracking and Electron Beam Con... Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline... Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process... Exploring electron beam induced atomic assembly via reinforcement learning in a molecular dynamics environment... Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding... Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy... Pagination Current page 1 Page 2 Next page ›› Last page Last » Key Links Google Scholar ORCID Organizations Computing and Computational Sciences Directorate Computational Sciences and Engineering Division Advanced Computing Methods for Physical Sciences Section Computational Chemistry and Nanomaterials Sciences Group
