[2605.06839] LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments

Condensed Matter > Materials Science arXiv:2605.06839 (cond-mat) [Submitted on 7 May 2026] Title:LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments Authors:Boris Slautin, Utkarsh Pratiush, Yu Liu, Kamyar Barakati, Sergei Kalinin View a PDF of the paper titled LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments, by Boris Slautin and 4 other authors View PDF Abstract:Autonomous experimentation has transformed microscopy and materials discovery by enabling closed-loop optimization including imaging and spectroscopy tuning, strucutre property relationship discovery, and exploration of combinatorial libraries. However, most current workflows remain limited to selecting measurements within fixed objective or hypothesis spaces, rather than generating new physical models from experimental data. Here, we introduce an open hypothesis-learning framework that combines symbolic regression with large-language-model-based physical evaluation and implement it for autonomous scanning probe microscopy. Symbolic regression generates candidate analytical relationships directly from sparse measurements, while the language-model evaluator ranks these candidates according to physical plausibility, scaling behavior, and consistency with known mechanisms. We demonstrate the approach on autonomous piezoresponse force microscopy measurements of ferroelectric domain switching in a PZT thin film. Starting from five seed measu...