Patents by Inventor Brian Rohr

Brian Rohr has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Methods and systems for machine-learning-assisted discovery of dark electrocatalysts and photo-electrocatalysts
    Patent number: 11493475
    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.
    Type: Grant
    Filed: December 5, 2019
    Date of Patent: November 8, 2022
    Assignees: Toyota Research Institute, Inc., California Institute of Technology
    Inventors: Santosh Suram, John M. Gregoire, Brian Rohr, Helge Stein
  • METHODS AND SYSTEMS FOR MACHINE-LEARNING-ASSISTED DISCOVERY OF DARK ELECTROCATALYSTS AND PHOTO-ELECTROCATALYSTS
    Publication number: 20200340941
    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.
    Type: Application
    Filed: December 5, 2019
    Publication date: October 29, 2020
    Inventors: Santosh Suram, John M. Gregoire, Brian Rohr, Helge Stein