ArticlePatterns (New York, N.Y.)2023
Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 32 citations in OpenAlex.
- afspm: A framework for manufacturer-agnostic automation in scanning probe microscopy.Beilstein journal of nanotechnology · 2026Article
- Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.Nanophotonics (Berlin, Germany) · 2025Review
- A Novel Deep Reinforcement Learning Approach for Dynamic Proportional-Integral Control in Scanning Probe Microscopy.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Real-time experiment-theory closed-loop interaction for autonomous materials science.Science advances · 2025Article
- Science acceleration and accessibility with self-driving labs.Nature communications · 2025Review
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership.Nature communications · 2024Article
- When Machine Learning Meets 2D Materials: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Explainability and human intervention in autonomous scanning probe microscopy.Patterns (New York, N.Y.) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using hypothesis-learning-driven automated scanning probe microscopy (SPM), we explore the bias-induced transformations that underpin the functionality of broad classes of devices and materials from batteries and memristors to ferroelectrics and antiferroelectrics. Optimization and design of these materials require probing the mechanisms of these transformations on the nanometer scale as a function of a broad range of control parameters, leading to experimentally intractable scenarios. Meanwhile, often these behaviors are understood within potentially competing theoretical hypotheses. Here, we develop a hypothesis list covering possible limiting scenarios for domain growth in ferroelectric materials, including thermodynamic, domain-wall pinning, and screening limited. The hypothesis-driven SPM autonomously identifies the mechanisms of bias-induced domain switching, and the results indicate that domain growth is ruled by kinetic control. We note that the hypothesis learning can be broadly used in other automated experiment settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.