7 citations · 8 across the 13 of their papers we have counts for
14 papers
Human-agent discovery of reconfigurable in-plane ferroelectric superdomain control
Yu Liu, Boris Slautin, Ching-Che Lin +3
Automated experimentation is most effective when the observables, available actions, and objective are defined before the experiment starts, as is the case for Bayesian optimizatio…
Hierarchical automation of scanning probe microscopy through agentic orchestration and algorithmic control
Boris N. Slautin, Sheryl L. Sanchez, Aidan Swanger +5
Rapid advances in agentic artificial intelligence enable scientific systems to interpret open-ended objectives, combine heterogeneous information, invoke specialized tools, and rev…
Beyond FAIR Data: Instrument Traces for Active and Autonomous Scientific Experimentation
Sergei V. Kalinin, Boris N. Slautin, Yu Liu +1
Artificial intelligence is turning scientific instruments into active systems in which observations can determine what is measured next. We argue that this creates an additional sc…
Closed-loop discovery of out-of-distribution processing protocols by evolutionary search and uncertainty-aware learning
Yu Liu, Stanislav Udovenko, Ching-Che Lin +4
Many materials and chemical systems exhibit history-dependent responses, where functional outcomes are governed not only by final-state variables but by the time-dependent sequence…
LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments
Boris Slautin, Utkarsh Pratiush, Yu Liu +2
Autonomous experimentation has transformed microscopy and materials discovery by enabling closed-loop optimization including imaging and spectroscopy tuning, strucutre property rel…
Mechanism-Resolved PFM of Ferroionic and Ferroelectric Responses in Thickness-Gradient Hf0.5Zr0.5O2 Libraries
Yu Liu, Yi-Xiu Chen, Haotong Liang +2
Resolving growth mechanisms and thickness evolution of functional properties is one of the key tasks in materials discovery and optimization involving thin-film materials, traditio…
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy
Yu Liu, Utkarsh Pratiush, Kamyar Barakati +5
Ferroelectric polarization switching underpins the functional performance of a wide range of materials and devices, yet its dependence on complex local microstructural features ren…
Exploring Domain Wall Pinning in Ferroelectrics via Automated High Throughput AFM
Kamyar Barakati, Yu Liu, Hiroshi Funakubo +1
Domain-wall dynamics in ferroelectric materials are strongly position-dependent since each polar interface is locked into a unique local microstructure. This necessitates spatially…
The Power of the Pareto Front: Balancing Uncertain Rewards for Adaptive Experimentation in scanning probe microscopy
Yu Liu, Sergei V. Kalinin
Automated experimentation has the potential to revolutionize scientific discovery, but its effectiveness depends on well-defined optimization targets, which are often uncertain or…
Rewards-based image analysis in microscopy
Kamyar Barakati, Yu Liu, Utkarsh Pratiush +2
Imaging and hyperspectral data analysis is central to progress across biology, medicine, chemistry, and physics. The core challenge lies in converting high-resolution or high-dimen…
Reward driven workflows for unsupervised explainable analysis of phases and ferroic variants from atomically resolved imaging data
Kamyar Barakati, Yu Liu, Chris Nelson +4
Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspect…
Reward based optimization of resonance-enhanced piezoresponse spectroscopy
Yu Liu, Boris Slautin, Jason Bemis +6
Dynamic spectroscopies in Scanning Probe Microscopy (SPM) are critical for probing material properties, such as force interactions, mechanical properties, polarization switching, a…
Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy
Yu Liu, Roger Proksch, Jason Bemis +8
Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not…
Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation
Yu Liu, Utkarsh Pratiush, Jason Bemis +8
The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key ele…