2 citations · 6 across the 11 of their papers we have counts for
11 papers
Physics-Informed Sequential Reconstruction of Scanning Probe Microscopy Images with Calibrated Uncertainty
Surya Prakash Reddy, Sergei V. Kalinin
Scanning probe microscopy (SPM) has become a mainstay of materials science, condensed matter physics, nanotechnology, biology, and semiconductor metrology. An SPM image, however, i…
Multitask Scanning Probe Microscopy
Aditya Raghavan, Yu Liu, Ian Mercer +2
Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials. Its increasing use for wafer-sca…
Beyond Directed Acyclic Graphs: Causal Zeros and Causal Differential Equations
Sergei V. Kalinin
Pearl's structural causal model (SCM) framework, built on directed acyclic graphs (DAGs) and the do-calculus, is the dominant formal language for causal reasoning. Yet it carries t…
From Closed-Loop Optimization to Open Decision Making: Coupled Digital Twins for Predictive and Autonomous Microscopy
Yu Liu, Boris Slautin, Ian Mercer +2
Automated experimentation is moving from closed-loop optimization toward open decision-making, where human or AI planners must forecast the consequences of candidate actions before…
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…
Causal Discovery from Data Assisted by Large Language Models
Kamyar Barakati, Alexander Molak, Chris Nelson +3
Knowledge driven discovery of novel materials necessitates the development of the causal models for the property emergence. While in classical physical paradigm the causal relation…
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.…
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…
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…
Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders
Mani Valleti, Yongtao Liu, Sergei Kalinin
Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functiona…