Knowledge extraction in catalysis utilizing design of experiments and machine learning-Reference-Cited by-同舟云学术

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Knowledge extraction in catalysis utilizing design of experiments and machine learning

Published:2022-06 Issue: Volume:36 Page:100781
ISSN:2211-3398
Container-title:Current Opinion in Chemical Engineering
language:en
Short-container-title:Current Opinion in Chemical Engineering

Author:

MacQueen Blake^ORCID,Jayarathna Rasika^ORCID,Lauterbach Jochen^ORCID

Publisher

Elsevier BV

Subject

General Energy

Reference57 articles.

1. Developments in the design of experiments;Atkinson;Int Stat Rev,1982

2. The role of machine learning in the understanding and design of materials;Moosavi;J Am Chem Soc,2020

3. Catalyze materials science with machine learning;Kim;ACS Mater Lett,2021

4. Box-behnken design: an alternative for the optimization of analytical methods;Ferreira;Anal Chim Acta,2007

5. Optimum reaction conditions for 1,4-Anhydroerythritol and xylitol hydrodeoxygenation over a ReOx-Pd/CeO2 catalyst via design of experiments;MacQueen;Ind Eng Chem Res,2019

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1. From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design;ACS Catalysis;2024-07-24

2. Design‐of‐Experiments‐Based Optimisation of Vanillin Yield from Kraft Lignin Using Pulse Electrolysis and Thermolysis;ChemElectroChem;2024-03-20

3. Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design;Advanced Science;2024-03-13

4. Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites;ACS Applied Engineering Materials;2024-02-02

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