56 citations · 81 across the 10 of their papers we have counts for
10 papers
Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materials
Chih-Yu Lee, Haotong Liang, Ryan Kim +4
Autonomous labs enable the integration of automated experiment execution, data analysis and decision making. The main challenge remains the integration of diverse data streams from…
Autonomous Small-Angle Scattering for Accelerated Soft Material Formulation Optimization
Tyler B. Martin, Duncan R. Sutherland, Austin McDannald +2
The pace of soft material formulation (re)development and design is rapidly increasing as both consumers and new legislation demand products that do less harm to the environment wh…
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…
Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning
A. Gilad Kusne, Austin McDannald, Brian DeCost
Autonomous materials research labs require the ability to combine and learn from diverse data streams. This is especially true for learning material synthesis-process-structure-pro…
Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping
Felix Adams, Austin McDannald, Ichiro Takeuchi +1
Autonomous experimentation (AE) combines machine learning and research hardware automation in a closed loop, guiding subsequent experiments toward user goals. As applied to materia…
Scalable Multi-Agent Lab Framework for Lab Optimization
A. Gilad Kusne, Austin McDannald
Autonomous materials research systems allow scientists to fail smarter, learn faster, and spend less resources in their studies. As these systems grow in number, capability, and co…
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 Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation
Logan Saar, Haotong Liang, Alex Wang +4
The next generation of physical science involves robot scientists - autonomous physical science systems capable of experimental design, execution, and analysis in a closed loop. Su…
Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping
A. Gilad Kusne, Austin McDannald, Brian DeCost +5
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, scien…
On-the-fly Autonomous Control of Neutron Diffraction via Physics-Informed Bayesian Active Learning
Austin McDannald, Matthias Frontzek, Andrei T. Savici +8
Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and dynamics of materials. However, instruments at neutron scattering fac…