18 papers
Beyond FAIR Data: Instrument Traces for Active and Autonomous Scientific Experimentation
Sergei V. Kalinin, Boris N. Slautin, Yu Liu +1
Artificial intelligence is turning scientific instruments into active systems in which observations can determine what is measured next. We argue that this creates an additional sc…
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…
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…