Abstract: A composition of matter includes an n-type semiconductor. At least a portion of the semiconductor has the crystal structure of the chemical compound represented by FeWO4. The portion of the semiconductor having the crystal structure of FeWO4 includes iron and tungsten. A photoanode can have a light-absorbing layer that includes or consists of the semiconductor. A solar fuels generator can include the photoanode.

Abstract: Methods and systems described herein concern machine-learning-assisted materials discovery. One embodiment selects a candidate sample set including a plurality of compositions and performs the following operations iteratively: (1) selects an acquisition sample set, (2) performs a dark electrocatalyst experiment or a photo-electrocatalyst experiment on the compositions in the acquisition sample set to determine one or more properties, (3) trains a machine learning model using the one or more properties, and (4) predicts, based at least in part on one or more outputs of the machine learning model, the one or more properties for one or more compositions in a test sample set including compositions on which an experiment has not yet been performed. When one or more predetermined termination criteria have been satisfied, the embodiment also identifies one or more compositions in the candidate sample set for which the one or more properties satisfy predetermined performance criteria.

Abstract: A composition of matter includes an n-type semiconductor. At least a portion of the semiconductor has the crystal structure of the chemical compound represented by FeWO4. The portion of the semiconductor having the crystal structure of FeWO4 includes iron and tungsten. A photoanode can have a light-absorbing layer that includes or consists of the semiconductor. A solar fuels generator can include the photoanode.

Abstract: Methods and systems described herein concern machine-learning-assisted materials discovery. One embodiment selects a candidate sample set including a plurality of compositions and performs the following operations iteratively: (1) selects an acquisition sample set, (2) performs a dark electrocatalyst experiment or a photo-electrocatalyst experiment on the compositions in the acquisition sample set to determine one or more properties, (3) trains a machine learning model using the one or more properties, and (4) predicts, based at least in part on one or more outputs of the machine learning model, the one or more properties for one or more compositions in a test sample set including compositions on which an experiment has not yet been performed. When one or more predetermined termination criteria have been satisfied, the embodiment also identifies one or more compositions in the candidate sample set for which the one or more properties satisfy predetermined performance criteria.