109 citations · 519 across the 47 of their papers we have counts for
47 papers
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities
Arpan Biswas, Sai Mani Prudhvi Valleti, Rama Vasudevan +2
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often non-differentiable parameter spaces, such as phase diagrams…
When the atoms dance: exploring mechanisms of electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy
Matthew G. Boebinger, Ayana Ghosh, Kevin M. Roccapriore +6
Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In thi…
Physics-driven discovery and bandgap engineering of hybrid perovskites
Sheryl L. Sanchez, Elham Foadian, Maxim Ziatdinov +4
The unique aspect of the hybrid perovskites is their tunability, allowing to engineer the bandgap via substitution. From application viewpoint, this allows creation of the tandem c…
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy
Sergei V. Kalinin, Yongtao Liu, Arpan Biswas +5
Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-…
Ferroelectric Schottky diodes of CuInPS nanosheet
Jinyuan Yao, Yongtao Liu, Shaoqing Ding +4
Ferroelectricity in van der Waals (vdW) layered material has attracted a great deal of interest recently. CuInPS (CIPS), the only vdW layered material whose ferroelectricit…
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments
Arpan Biswas, Yongtao Liu, Nicole Creange +6
Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements…
Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore +9
Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is…
Sub-10 nm Probing of Ferroelectricity in Heterogeneous Materials by Machine Learning Enabled Contact Kelvin Probe Force Microscopy
Sebastian W. Schmitt, Rama K. Vasudevan, Maurice Seifert +4
Reducing the dimensions of ferroelectric materials down to the nanoscale has strong implications on the ferroelectric polarization pattern and on the ability to switch the polariza…
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian +72
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasin…
Deep Kernel Methods Learn Better: From Cards to Process Optimization
Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov +1
The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and projec…
Revealing intrinsic vortex-core states in Fe-based superconductors through machine-learning-driven discovery
Yueming Guo, Hu Miao, Qiang Zou +5
Electronic states within superconducting vortices hold crucial information about paring mechanisms and topology. While scanning tunneling microscopy/spectroscopy(STM/S) can image t…
Post-Experiment Forensics and Human-in-the-Loop Interventions in Explainable Autonomous Scanning Probe Microscopy
Yongtao Liu, Maxim Ziatdinov, Rama Vasudevan +1
The broad adoption of machine learning (ML)-based automated and autonomous experiments (AE) in physical characterization and synthesis requires development of strategies for unders…
Designing Workflows for Materials Characterization
Sergei V. Kalinin, Maxim Ziatdinov, Mahshid Ahmadi +4
Experimental science is enabled by the combination of synthesis, imaging, and functional characterization. Synthesis of a new material is typically followed by a set of characteriz…
Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis
Arpan Biswas, Maxim Ziatdinov, Sergei V. Kalinin
Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as EELS or 4D STEM, that contain information on a wide range o…
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space
Ayana Ghosh, Sergei V. Kalinin, Maxim A. Ziatdinov
Discovery of the molecular candidates for applications in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries, necessitates development…
Exploring the microstructural origins of conductivity and hysteresis in metal halide perovskites via active learning driven automated scanning probe microscopy
Yongtao Liu, Jonghee Yang, Rama K. Vasudevan +4
Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These p…
Disentangling electronic transport and hysteresis at individual grain boundaries in hybrid perovskites via automated scanning probe microscopy
Yongtao Liu, Jonghee Yang, Benjamin J. Lawrie +4
Underlying the rapidly increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) is the advance in the understanding of the microstructure of polycrystall…
Enabling Autonomous Electron Microscopy for Networked Computation and Steering
Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran +6
Advanced electron microscopy workflows require an ecosystem of microscope instruments and computing systems possibly located at different sites to conduct remotely steered and auto…
Microscopy is All You Need
Sergei V. Kalinin, Rama Vasudevan, Yongtao Liu +3
We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tr…
A roadmap for edge computing enabled automated multidimensional transmission electron microscopy
Debangshu Mukherjee, Kevin M. Roccapriore, Anees Al-Najjar +8
The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine.…
Delineating complex ferroelectric domain structures via second harmonic generation spectral imaging
Wei Li, Yunpeng Ma, Tianyi Feng +3
Understanding the mechanisms and spatial correlations of crystallographic symmetry breaking in ferroelectric materials is essential to tuning their functional properties. While opt…
Learning and predicting photonic responses of plasmonic nanoparticle assemblies via dual variational autoencoders
Muammer Y. Yaman, Sergei V. Kalinin, Kathryn N. Guye +2
We demonstrate the application of machine learning for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational…
MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies
Zhuowen Zhao, Tanny Chavez, Elizabeth A. Holman +14
Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data p…
Probing electron beam induced transformations on a single defect level via automated scanning transmission electron microscopy
Kevin M. Roccapriore, Matthew G. Boebinger, Ondrej Dyck +4
The robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on the ensemble learning and iterative training (ELIT) of dee…