430 citations · 1.7k across the 114 of their papers we have counts for
118 papers
Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders
Boris N. Slautin, Kamyar Barakati, Hiroshi Funakubo +4
Microscopy techniques generate vast amounts of complex image data that in principle can be used to discover simpler, interpretable, and parsimonious forms to reveal the underlying…
Comparing Machine Learning and Physics-Based Nanoparticle Geometry Determinations Using Far-Field Spectral Properties
Mengqi Sun, Zixu Huang, Muammer Y. Yaman +3
Anisotropic metal nanostructures exhibit polarization-dependent light scattering. This property has been widely exploited to determine geometries of subwavelength structures using…
Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments
Ralph Bulanadi, Jawad Chowdhury, Funakubo Hiroshi +4
Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primari…
Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
Aditya Vatsavai, Ganesh Narasimha, Yongtao Liu +5
Rapidly determining structure-property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. I…
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model
Boris N. Slautin, Utkarsh Pratiush, Doru C. Lupascu +2
We introduce a Deep Kernel Learning Variational Autoencoder (VAE-DKL) framework that integrates the generative power of a Variational Autoencoder (VAE) with the predictive nature o…
Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries
Yu Liu, Aditya Raghavan, Utkarsh Pratiush +13
Combinatorial materials libraries provide a powerful platform for mapping how physical properties evolve across binary and ternary cross-sections of multicomponent phase diagrams.…
Reward driven workflows for unsupervised explainable analysis of phases and ferroic variants from atomically resolved imaging data
Kamyar Barakati, Yu Liu, Chris Nelson +4
Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspect…
Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors
Michael J. Kenney, Katerina G. Malollari, Sergei V. Kalinin +1
Lithium-ion batteries are a key energy storage technology driving revolutions in mobile electronics, electric vehicles and renewable energy storage. Capacity retention is a vital p…
Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments
Boris N. Slautin, Yu Liu, Jan Dec +4
We have developed a Bayesian optimization (BO) workflow that integrates intra-step noise optimization into automated experimental cycles. Traditional BO approaches in automated exp…
Evolution of ferroelectric properties in SmxBi1-xFeO3 via automated Piezoresponse Force Microscopy across combinatorial spread libraries
Aditya Raghavan, Rohit Pant, Ichiro Takeuchi +6
Combinatorial spread libraries offer a unique approach to explore evolution of materials properties over the broad concentration, temperature, and growth parameter spaces. However,…
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning
Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo +3
Scientific advancement is universally based on the dynamic interplay between theoretical insights, modelling, and experimental discoveries. However, this feedback loop is often slo…
Multiscale structure-property discovery via active learning in scanning tunneling microscopy
Ganesh Narasimha, Dejia Kong, Paras Regmi +4
Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. The local structures are conventionally probed using spatially resolved stud…
Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS
Utkarsh Pratiush, Kevin M. Roccapriore, Yongtao Liu +3
Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron e…
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries
Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov +6
The rapid growth of automated and autonomous instrumentations brings forth an opportunity for the co-orchestration of multimodal tools, equipped with multiple sequential detection…
Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings
Ayana Ghosh, Maxim Ziatdinov, Sergei V. Kalinin
As vast databases of chemical identities become increasingly available, the challenge shifts to how we effectively explore and leverage these resources to study molecular propertie…
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…
Autonomous convergence of STM control parameters using Bayesian Optimization
Ganesh Narasimha, Saban Hus, Arpan Biswas +2
Scanning Tunneling microscopy (STM) is a widely used tool for atomic imaging of novel materials and its surface energetics. However, the optimization of the imaging conditions is a…
Dynamic STEM-EELS for single atom and defect measurement during electron beam transformations
Kevin M. Roccapriore, Riccardo Torsi, Joshua Robinson +2
On- and off-axis electron energy loss spectroscopy (EELS) is a powerful method for probing local electronic structure on single atom level. However, many materials undergo electron…
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-…
Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability
Renan Souza, Tyler J. Skluzacek, Sean R. Wilkinson +2
Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Ed…
Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems
Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran +4
We propose a framework to develop cyber solutions to support the remote steering of science instruments and measurements collection over instrument-computing ecosystems. It is base…
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…