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

TL;DR

This paper presents an open hypothesis-learning framework for autonomous microscopy that combines symbolic regression with large-language-model evaluation to discover physical laws from experimental data.

Contribution

It introduces a novel integration of symbolic regression and language models for autonomous hypothesis generation and evaluation in microscopy experiments.

Findings

Successfully identified interpretable voltage-time growth laws in ferroelectric domain switching.

Extended autonomous microscopy from optimization to hypothesis discovery.

Framework can generate candidate physical laws directly from sparse measurements.

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 measurements, the workflow evolves from physically incomplete candidate expressions toward interpretable voltage-time growth laws consistent with kinetic domain-wall motion. This work extends autonomous microscopy from closed-loop optimization toward open hypothesis discovery, where candidate physical laws emerge from the experiment itself rather than being specified in advance. More broadly, the framework establishes a route for integrating symbolic regression, physical reasoning, and adaptive experimentation into hierarchical autonomous scientific workflows.