14 papers
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…
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…