Mechanisms for molecule design using machine learning include: forming a first training set for a neural network using, for each of a first plurality of known molecules, a plurality of input values that represent the structure of the known molecule and a plurality of functional property values for the known molecule; training the neural network using the first training set; proposing a first plurality of proposed molecules, and predicting first predicted functional property values of the first plurality of proposed molecules that have the desired function property values; causing the first plurality of proposed molecules to be synthesized to form a first plurality of synthesized molecules; receiving first measured functional property values of the first plurality of synthesized molecules; and adding data regarding the first plurality of synthesized molecules to the first training set to form a second training set and retrain the neural network using the second training set.