Researchers at Waseda, University, Japan have developed a machine learning workflow to optimize the output force of photo-actuated organic crystals.
Using LASSO (least absolute shrinkage and selection operator) regression to identify key molecular substructures and Bayesian optimization for efficient sampling, they achieved a maximum blocking force of 37.0 mN – 73 times more efficient than conventional methods.
These findings could help develop remote-controlled actuators for medical devices and robotics, supporting applications such as minimally invasive surgery and precision drug delivery. [Read more…] about Machine learning unlocks ‘superior performance’ in light-driven organic crystals