1 citations · 5 across the 17 of their papers we have counts for
18 papers
Synthesis and Characterization of Compositionally Complex (Gd/Ho/Er/Dy)2Zr2O7 Thin Film Combinatorial Library
Dalton A. Pearl, Jade Holliman, Reece Emory +13
High-throughput synthesis and characterization of novel ceramic materials with improved thermomechanical properties and phase stability are needed to accelerate the discovery of ne…
LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments
Boris Slautin, Utkarsh Pratiush, Yu Liu +2
Autonomous experimentation has transformed microscopy and materials discovery by enabling closed-loop optimization including imaging and spectroscopy tuning, strucutre property rel…
PATHFINDER: Multi-objective discovery in structural and spectral spaces
Kamyar Barakati, Boris N. Slautin, Utkarsh Pratiush +2
Automated decision-making is becoming key for automated characterization including electron and scanning probe microscopies and nano indentation. Most machine learning driven workf…
From Photons to Electrons: Accelerated Materials Discovery via Random Libraries and Automated Scanning Transmission Electron Microscopy
Boris Slautin, Kamyar Barakati, Utkarsh Pratiush +10
The real-world implementation of materials prediction algorithms remains limited by persistent characterization bottlenecks in materials discovery, where photon-based probe techniq…
Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy
Utkarsh Pratiush, Kamyar Barakati, Boris N. Slautin +4
Modern automated microscopy faces a fundamental discovery challenge: in many systems, the most important scientific information does not reside in the immediately visible image fea…
Autonomous Probe Microscopy with Robust Bag-of-Features Multi-Objective Bayesian Optimization: Pareto-Front Mapping of Nanoscale Structure-Property Trade-Offs
Kamyar Barakati, Haochen Zhu, C Charlotte Buchanan +3
Combinatorial materials libraries are an efficient route to generate large families of candidate compositions, but their impact is often limited by the speed and depth of character…
Bayesian Co-Navigation of a Computational Physical Model and AFM Experiment to Autonomously Survey a Combinatorial Materials Library
Boris N. Slautin, Kamyar Barakati, Yu Liu +3
Building autonomous experiment workflows requires transcending beyond the data-driven surrogate models to incorporate and dynamically refine physical theory during exploration. Her…
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…
SAM: Task-Adaptive SAM with Physics-Guided Rewards
Kamyar Barakati, Utkarsh Pratiush, Sheryl L. Sanchez +5
Image segmentation is a critical task in microscopy, essential for accurately analyzing and interpreting complex visual data. This task can be performed using custom models trained…
Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy
Utkarsh Pratiush, Austin Houston, Kamyar Barakati +70
Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with me…
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy
Yu Liu, Utkarsh Pratiush, Kamyar Barakati +5
Ferroelectric polarization switching underpins the functional performance of a wide range of materials and devices, yet its dependence on complex local microstructural features ren…
Exploring Domain Wall Pinning in Ferroelectrics via Automated High Throughput AFM
Kamyar Barakati, Yu Liu, Hiroshi Funakubo +1
Domain-wall dynamics in ferroelectric materials are strongly position-dependent since each polar interface is locked into a unique local microstructure. This necessitates spatially…
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…
Rewards-based image analysis in microscopy
Kamyar Barakati, Yu Liu, Utkarsh Pratiush +2
Imaging and hyperspectral data analysis is central to progress across biology, medicine, chemistry, and physics. The core challenge lies in converting high-resolution or high-dimen…
Materials Discovery in Combinatorial and High-throughput Synthesis and Processing: A New Frontier for SPM
Boris N. Slautin, Yongtao Liu, Kamyar Barakati +13
For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient,…
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…
Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments
Kamyar Barakati, Utkarsh Pratiush, Austin C. Houston +2
Automated experiments in scanning transmission electron microscopy (STEM) require rapid image segmentation to optimize data representation for human interpretation, decision-making…
Physics-based reward driven image analysis in microscopy
Kamyar Barakati, Hui Yuan, Amit Goyal +1
The rise of electron microscopy has expanded our ability to acquire nanometer and atomically resolved images of complex materials. The resulting vast datasets are typically analyze…