  Optical materials with special optical properties are widely used in a broad
span of technologies, from computer displays to solar energy utilization
leading to large dataset accumulated from years of extensive materials
synthesis and optical characterization. Previously, machine learning models
have been developed to predict the optical absorption spectrum from a materials
characterization image or vice versa. Herein we propose TLOpt, a transfer
learning based inverse optical materials design algorithm for suggesting
material compositions with a desired target light absorption spectrum. Our
approach is based on the combination of a deep neural network model and global
optimization algorithms including a genetic algorithm and Bayesian
optimization. A transfer learning strategy is employed to solve the small
dataset issue in training the neural network predictor of optical absorption
spectrum using the Magpie materials composition descriptor. Our extensive
experiments show that our algorithm can inverse design the materials
composition with stoichiometry with high accuracy.
