Domain-specialized small language models enable deterministic atomic-resolution scanning probe microscopy control with 99.3% command accuracy, lower computational cost, and better domain performance than larger general models.
and Mukherjee, Debangshu and Roccapriore, Kevin and Blaiszik, Benjamin J
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PtyRANNOSAUR uses convolutional autoencoders trained on crystal structure databases to map 4D-STEM ptychography data to sub-0.5 Å phase images 10-100x faster than iterative methods while handling partial coherence, multiple scattering, and scan errors.
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.
A trust-region Bayesian optimization framework integrates LEED multiple scattering models to jointly optimize structural and experimental parameters for automated surface reconstruction.
citing papers explorer
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Integrating Domain-Specialized Language Models with AI Measurement Tools for Deterministic Atomic-Resolution Experimentation
Domain-specialized small language models enable deterministic atomic-resolution scanning probe microscopy control with 99.3% command accuracy, lower computational cost, and better domain performance than larger general models.
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PtyRANNOSAUR: Ptychography with Robust Artificial Neural Networks Optimized for Sub-Angstrom Accuracy and Ultrafast Reconstruction
PtyRANNOSAUR uses convolutional autoencoders trained on crystal structure databases to map 4D-STEM ptychography data to sub-0.5 Å phase images 10-100x faster than iterative methods while handling partial coherence, multiple scattering, and scan errors.
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Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.
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Physics-informed automated surface reconstructing via low-energy electron diffraction based on Bayesian optimization
A trust-region Bayesian optimization framework integrates LEED multiple scattering models to jointly optimize structural and experimental parameters for automated surface reconstruction.