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Found 5 papers, 2 papers with code
Batch Bayesian Optimization for High-Dimensional Experimental Design: Simulation and Visualization
Bayesian Optimization (BO) is increasingly used to guide experimental optimization tasks.
Learning Relevant Contextual Variables Within Bayesian Optimization
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions.
Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization
High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors.
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up
While halide perovskites attract significant academic attention, examples of at-scale industrial production are still sparse.
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems.
