Equipping data-driven experiment planning for Self-driving Laboratories with semantic memory: case studies of transfer learning in chemical reaction optimization
Riley J. Hickman, Jurgis Ruža, Hermann Tribukait, Loı̈c M. Roch, Alberto García-Durán
Abstract
SeMOpt uses meta-/few-shot learning to enable knowledge transfer from previous experiments to accelerate Bayesian optimization of chemical reactions.
Topics & Concepts
Bayesian optimizationComputer scienceTransfer of learningArtificial intelligenceTransfer (computing)Bayesian probabilitySemantic memoryMachine learningPsychologyNeuroscienceCognitionParallel computingMachine Learning in Materials ScienceInnovative Microfluidic and Catalytic Techniques InnovationMicrofluidic and Capillary Electrophoresis Applications