Ayana Ghosh Research Scientist Contact . . | GHOSHA@ORNL.GOV All Publications Exploring electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy Mapping causal pathways with structural modes fingerprint for perovskite oxides Active causal learning for decoding chemical complexities with targeted interventions... Towards FAIR Workflows for Federated Experimental Sciences Insights into Prismatic Loop Formation in Irradiated Fe–Cr Alloys from Hypothesis-Driven Active Learning and Causal Analysis Visualizing the Amorphous to Crystalline Transition of Bismuth Selenide in the TEM Precision Defect Engineering in 2D Materials via Automated STEM Atomic Fabrication Integrating High-Performance Computing with Electron Microscopy for Scientific Insights 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 Bending-induced isostructural transitions in ultrathin layers of van der Waals ferrielectrics Identification of novel organic polar materials: A machine learning study with importance sampling Active Causal Machine Learning for Molecular Property Prediction Machine learning for automated experimentation in scanning transmission electron microscopy... Predictive Design of Hybrid Improper Ferroelectric Double Perovskite Oxides Stress and Curvature Effects in Layered 2D Ferroelectric CuInP2S6 Design of high polarization low switching barrier hybrid improper ferroelectric perovskite oxide superlattices Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space... Fabrication of Atomic-scale Defect Structures within 2D Materials through Automated Electron Beam Control Switching of Hybrid Improper Ferroelectricity in Oxide Double Perovskites Unsupervised machine learning discovery of structural units and transformation pathways from imaging data Probe microscopy is all you need AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy Pagination Current page 1 Page 2 Next page ›â¶Äº Last page Last » Key Links Curriculum Vitae Organizations Computing and Computational Sciences Directorate Computational Sciences and Engineering Division Advanced Computing Methods for Physical Sciences Section Computational Chemistry and Nanomaterials Sciences Group
