Publications

List of publications of automated and autonomous microscopy that take advantage of AEcroscopy or can be done with AEcroscopy.

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

• 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).

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

• 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).

• 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.