About Autonomous Experiment

About Autonomous Experiment#

\(_{Yongtao}\) \(_{Liu,}\) \(_{youngtaoliu@gmail.com}\)

\(_{March}\) \(_{2026}\)

Autonomous experiments integrate artificial intelligence (AI) and machine learning (ML) for real-time data analysis, decision-making, optimization, and adaptation to enable experiments with minimal or no human intervention. Unlike automated experiments that follow a fixed script, autonomous experiments learn from each measurement and adjust subsequent steps accordingly.

In this chapter, we introduce ML-driven experiment workflows constructed with AEcroscoPy for autonomous microscopy. The workflows in this chapter are grounded in the following papers:

  1. Accelerating Structure-Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy Gong, J. et al. arXiv, 2026. Combines autonomous microscopy with dual-novelty deep kernel learning and a dual variational autoencoder to uncover structure-property relationships in halide perovskite films via conductive AFM.

  2. Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments ACS Nanoscience Au, 2025. Introduces INS2ANE, a framework that integrates novelty scoring with strategic sampling to discover unexpected phenomena in autonomous microscopy experiments, going beyond standard optimization targets.

  3. Scientific Exploration with Expert Knowledge (SEEK) in Autonomous Scanning Probe Microscopy with Active Learning Pratiush, U. et al. Digital Discovery, 2025. Develops constrained active learning approaches that incorporate prior expert knowledge into deep kernel learning for more efficient and guided autonomous SPM exploration.

  4. SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery Biswas, A. et al. Digital Discovery, 2025. Presents a Bayesian optimization method with a cost-driven acquisition function and dynamic constraint gate for discovering multiple optimal regions in noisy, multimodal piezoresponse parameter spaces.

  5. Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy Vatsavai, A. et al. Digital Discovery, 2025. Introduces curiosity-driven algorithms with deep learning surrogate models to actively sample regions with unexplored structure-property correlations in ferroelectric materials.

  6. Evolution of Ferroelectric Properties in SmxBi1–xFeO3 via Automated Piezoresponse Force Microscopy across Combinatorial Spread Libraries Raghavan, A. et al. ACS Nano, 2024. Applies automated PFM to combinatorial spread libraries, exploring the ferroelectric–antiferroelectric morphotropic phase boundary in SmxBi1–xFeO3 with quantitative, automated measurement protocols.

We also recommend visit DKGP about deep kernel Gaussian Process, an open-source library combining deep neural networks with Gaussian Processes for uncertainty-aware regression and Bayesian optimization with active learning acquisition functions (Expected Improvement, UCB, Thompson Sampling).