US 12,112,836 B2
Artificial intelligence directed zeolite synthesis
Larry A. Bowden, Jr., Houston, TX (US); Dan Xie, El Cerrito, CA (US); Christopher Michael Lew, Almeda, CA (US); and Joel Edward Schmidt, Oakland, CA (US)
Assigned to CHEVRON U.S.A. INC., San Ramon, CA (US)
Filed by Chevron U.S.A. Inc., San Ramon, CA (US)
Filed on Jun. 14, 2021, as Appl. No. 17/346,463.
Prior Publication US 2022/0399085 A1, Dec. 15, 2022
Int. Cl. G16C 20/70 (2019.01); G16C 20/10 (2019.01); G16C 20/40 (2019.01); G16C 20/50 (2019.01); G16C 60/00 (2019.01); B01J 29/04 (2006.01)
CPC G16C 20/70 (2019.02) [G16C 20/10 (2019.02); G16C 20/40 (2019.02); G16C 20/50 (2019.02); G16C 60/00 (2019.02); B01J 29/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer implemented method for catalyst construction, the method comprising:
extracting historical data including historic chemical reaction data and historic catalyst construction yield data;
converting the historic chemical reaction data into graph models to represent molecular structure data;
incorporating the graph models representing the molecular structure data into a chemical reaction algorithm;
training a vectorized cognitive deep learning network of the chemical reaction algorithm by using the graph models and a property of the historic chemical reaction data to produce a catalyst chemical reaction model;
validating the catalyst chemical reaction model by inputting the historic chemical reaction data and comparing a generated property corresponding to the catalyst chemical reaction model to the property of the historic chemical reaction data; and
updating, based on a difference between the generated property and the property of the historic chemical reaction data being larger than a threshold value, the training of the catalyst chemical reaction model.