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ARXIV:2605.06839 · SCIENTIFIC DISCOVERY · SUBMITTED 11 MAY · 20:45 UTC · FRESHNESS STALE
ARXIV:2605.06839SCIENTIFIC DISCOVERYSUBMITTED 11 MAY · 20:45 UTCFRESHNESS STALEBoris Slautin · Utkarsh Pratiush · Yu Liu · Kamyar Barakati · Sergei Kalinin · arXiv
An LLM-guided framework autonomously discovers new physical laws from microscopy experiments, moving beyond fixed hypotheses to open-ended scientific exploration.
Opportunity summary
Pain An LLM-guided framework autonomously discovers new physical laws from microscopy experiments, moving beyond fixed hypotheses to open-ended scientific exploration.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
An LLM-guided framework autonomously discovers new physical laws from microscopy experiments, moving beyond fixed hypotheses to open-ended scientific exploration. However, most current workflows remain limited to selecting measurements within fixed objective or hypothesis spaces,…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We demonstrate the approach on autonomous piezoresponse force microscopy measurements of ferroelectric domain switching in a PZT thin film. Code availability is flagged in…
Scientific Discovery moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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An LLM-guided framework autonomously discovers new physical laws from microscopy experiments, moving beyond fixed hypotheses to open-ended scientific exploration.
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10.48550/arXiv.2605.06839An LLM-guided framework autonomously discovers new physical laws from microscopy experiments, moving beyond fixed hypotheses to open-ended scientific exploration.
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.
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PROBLEM
An LLM-guided framework autonomously discovers new physical laws from microscopy experiments, moving beyond fixed hypotheses to open-ended scientific exploration. However, most current workflows remain limited to selecting measurements within fixed objective or hypothesis spaces...
METHOD
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 re...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We demonstrate the approach on autonomous piezoresponse force microscopy measurements of ferroelectric domain switching in a PZT thin film. Code availability is flagged in the production record; the publi...
WHY NOW
Scientific Discovery moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
An LLM-guided framework autonomously discovers new physical laws from microscopy experiments, moving beyond fixed hypotheses to open-ended scientific exploration. However, most current workflows remain limited to selecting measurements within fixed objective or hypothesis spaces, rather than generating new physical models from experimental data.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We demonstrate the approach on autonomous piezoresponse force microscopy measurements of ferroelectric domain switching in a PZT thin film. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Scientific Discovery moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An LLM-guided framework autonomously discovers new physical laws from microscopy experiments, moving beyond fixed hypotheses to open-ended scientific exploration.
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