Publications

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

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

This chapter lists peer-reviewed publications and preprints on automated and autonomous scanning probe microscopy that are built on, demonstrate, or are reproducible with AEcroscoPy. The works span a range of topics including active learning, deep kernel learning, hypothesis-driven experimentation, agentic AI, and high-throughput characterization of ferroelectric and perovskite materials.

2026

• Gong, Jiawei, et al. “Accelerating Structure-Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy.” arXiv:2603.17028 (2026).

2025

• Liu, Yongtao, et al. “Polarization Switching on the Open Surfaces of the Wurtzite Ferroelectric Nitrides: Ferroelectric Subsystems and Electrochemical Reactivity.” Advanced Materials (2025).

• “Autonomous Multistate Nanoencoding Using Combinatorial Ferroelectric Closure Domains in BiFeO₃.” ACS Nano (2025).

• “Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments.” ACS Nanoscience Au (2025).

• Pratiush, Utkarsh, et al. “Scientific Exploration with Expert Knowledge (SEEK) in Autonomous Scanning Probe Microscopy with Active Learning.” Digital Discovery 4 (2025): 252–263.

• Vatsavai, Aditya, et al. “Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy.” Digital Discovery 4 (2025): 2188–2197.

• Biswas, Arpan, et al. “SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-Modal and Non-Differentiable Black-Box Functions.” Digital Discovery 4 (2025): 853–867.

• Bulanadi, Ralph, et al. “Auto-3DPFM: Automating Polarization-Vector Mapping at the Nanoscale.” arXiv:2512.09249 (2025).

2024

• Raghavan, Aditya, et al. “Evolution of Ferroelectric Properties in SmxBi1–xFeO3 via Automated Piezoresponse Force Microscopy across Combinatorial Spread Libraries.” ACS Nano 18.37 (2024): 25591–25600.

• Yang, J., et al. “Coexistence and Interplay of Two Ferroelectric Mechanisms in Zn₁₋ₓMgₓO.” Advanced Materials (2024).

• “On-demand nanoengineering of in-plane ferroelectric topologies.” Nature Nanotechnology (2024).

• Smith, Benjamin, et al. “Physics-informed models of domain wall dynamics as a route for autonomous domain wall design via reinforcement learning.” Digital Discovery (2024).

• Biswas, Arpan, et al. “A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments.” npj Computational Materials 10.1 (2024).

• Liu, Yongtao, et al. “Synergizing Human Expertise and AI Efficiency with Language Model for Microscopy Operation and Automated Experiment Design.” arXiv:2401.13803 (2024).

2023

• Liu, Yongtao, et al. “AEcroscoPy: A software-hardware framework empowering microscopy toward automated and autonomous experimentation.” arXiv:2312.10281 (2023).

• Liu, Yongtao, et al. “Disentangling electronic transport and hysteresis at individual grain boundaries in hybrid perovskites via automated scanning probe microscopy.” ACS Nano (2023).

• Liu, Yongtao, et al. “Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy.” npj Computational Materials 9.1 (2023): 34.

• Liu, Yongtao, et al. “Exploring the Relationship of Microstructure and Conductivity in Metal Halide Perovskites via Active Learning-Driven Automated Scanning Probe Microscopy.” The Journal of Physical Chemistry Letters 14.13 (2023): 3352–3359.

• Liu, Yongtao, et al. “Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials.” Patterns 4.3 (2023).

2022

• Liu, Yongtao, et al. “Experimental discovery of structure–property relationships in ferroelectric materials via active learning.” Nature Machine Intelligence 4.4 (2022): 341–350.

• Ziatdinov, Maxim A., et al. “Hypothesis learning in automated experiment: application to combinatorial materials libraries.” Advanced Materials 34.20 (2022): 2201345.

• Liu, Yongtao, et al. “Exploring physics of ferroelectric domain walls in real time: deep learning enabled scanning probe microscopy.” Advanced Science 9.31 (2022): 2203957.

• Liu, Yongtao, et al. “Exploring leakage in dielectric films via automated experiments in scanning probe microscopy.” Applied Physics Letters 120.18 (2022).

2021

• Vasudevan, Rama K., et al. “Autonomous experiments in scanning probe microscopy and spectroscopy: choosing where to explore polarization dynamics in ferroelectrics.” ACS Nano 15.7 (2021): 11253–11262.