7 citations · 8 across the 6 of their papers we have counts for
10 papers · 1 filter
Autonomous epitaxial atomic-layer synthesis via real-time computer vision of electron diffraction
Haotong Liang, Yunlong Sun, Ryan Paxson +8
Autonomous science platforms which make decisions on the fly are fundamentally changing the outlook for materials development. AI-driven schemes can effectively reduce the total nu…
Quantum Kernel Machine Learning for Autonomous Materials Science
Felix Adams, Daiwei Zhu, David W. Steuerman +2
Autonomous materials science, where active learning is used to navigate large compositional phase space, has emerged as a powerful vehicle to rapidly explore new materials. A cruci…
Real-time experiment-theory closed-loop interaction for autonomous materials science
Haotong Liang, Chuangye Wang, Heshan Yu +6
Iterative cycles of theoretical prediction and experimental validation are the cornerstone of the modern scientific method. However, the proverbial "closing of the loop" in experim…
Benchmarking Active Learning Strategies for Materials Optimization and Discovery
Alex Wang, Haotong Liang, Austin McDannald +2
Autonomous physical science is revolutionizing materials science. In these systems, machine learning controls experiment design, execution, and analysis in a closed loop. Active le…
A Semi-Supervised Approach for Automatic Crystal Structure Classification
Satvik Lolla, Haotong Liang, A. Gilad Kusne +2
The structural solution problem can be a daunting and time consuming task. Especially in the presence of impurity phases, current methods such as indexing become more unstable. In…
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
A. Gilad Kusne, Heshan Yu, Changming Wu +13
Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to gu…