“…To give a few concrete examples, various ML methods have now been demonstrated for a wide range of systems such as molecular and materials science studies 3 , for use in optical communications and photonics 4 , to accurately predict battery life 5 , to accelerate lattice Monte Carlo simulations using neural networks 6 , for studying complex networks 7 , for characterizing surface microstructure of complex materials 8 , for chemical discovery 9 , for active matter analysis by using deep neural networks to track objects 10 , for particle physics 11 , for antimicrobial studies 12 , for pattern recognition for optical microscopy images of metallurgical microstructures 13 , for learning Perovskit bandgaps 14 , for real-time mapping of electron backscatter diffraction (EBSD) patterns to crystal orientations 15 , for speeding up simulation-based accelerator optimization studies 16 , for Bayesian optimization of free electron lasers (FEL) 17 , for temporal power reconstruction of FELs 18 , for various applications at the Large Hadron Collider (LHC) at CERN including optics corrections and detecting faulty beam position monitors [19][20][21] , for reconstruction of a storage ring's linear optics based on Bayesian inference 22 , to analyze beam position monitor placement in accelerators to find arrangements with the lowest probable predictive errors based on Bayesian Gaussian regression 23 , for temporal shaping of electron bunches in particle accelerators 24 , for stabilization of source properties in synchrotron light sources 25 , and to represent many-body interactions with restricted-Boltzmann-machine neural networks 26 .…”