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Found 6 papers, 1 papers with code
Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning
Autonomous materials research labs require the ability to combine and learn from diverse data streams.
Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping
Here, we present a set of methods for integrating human input into an autonomous materials exploration campaign for composition-structure phase mapping.
Scalable Multi-Agent Lab Framework for Lab Optimization
We demonstrate this framework with an autonomous material science lab in mind - where information from diverse research campaigns can be combined to ad-dress the scientific question at hand.
Benchmarking Active Learning Strategies for Materials Optimization and Discovery
In these systems, machine learning controls experiment design, execution, and analysis in a closed loop.
A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation
We present the next generation in science education, a kit for building a low-cost autonomous scientist.
Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades.
