7 citations · 9 across the 4 of their papers we have counts for
5 papers
Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy
Jawad Chowdhury, Ganesh Narasimha, Jan-Chi Yang +4
Autonomous experimental systems are increasingly used in materials research to accelerate scientific discovery, but their performance is often limited by low-quality, noisy data. T…
Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
Aditya Vatsavai, Ganesh Narasimha, Yongtao Liu +5
Rapidly determining structure-property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. I…
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…
AEcroscoPy: A software-hardware framework empowering microscopy toward automated and autonomous experimentation
Yongtao Liu, Kevin Roccapriore, Marti Checa +4
Microscopy, in particular scanning probe and electron microscopy, has been pivotal in improving our understanding of structure-function relationships at the nanoscale and is by now…