Generalizability and limitations of machine learning for yield prediction of oxidative coupling of methane-Reference-Cited by-同舟云学术

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Generalizability and limitations of machine learning for yield prediction of oxidative coupling of methane

Published:2022-03 Issue: Volume:2 Page:100013
ISSN:2772-5081
Container-title:Digital Chemical Engineering
language:en
Short-container-title:Digital Chemical Engineering

Author:

Siritanaratkul Bhavin

Publisher

Elsevier BV

Reference36 articles.

1. Balance between model detail and experimental information in steam methane reforming over a Ni/MgO-SiO2 catalyst;Devocht;AlChE J.,2019

2. Reaction mechanism generator v3.0: advances in automatic mechanism generation;Liu;J. Chem. Inf. Model.,2021

3. Quantifying the impact of parametric uncertainty on automatic mechanism generation for CO2 hydrogenation on Ni(111);Kreitz;JACS Au,2021

4. Machine learning for catalysis informatics: recent applications and prospects;Toyao;ACS Catal.,2020

5. Accelerated discovery of CO2 electrocatalysts using active machine learning;Zhong;Nature,2020

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Model-based catalyst screening and optimal experimental design for the oxidative coupling of methane;Digital Chemical Engineering;2024-09

2. Towards a Better Understanding of Rainfed Wheat Yield Predictions in Arid and Semi-Arid Regions: How Early, Precise, and Transferable Can These Models Be?;2024

3. Editorial: Machine learning for chemical processes;Digital Chemical Engineering;2022-12

4. Sulfur oxidative coupling of methane process development and its modeling via machine learning;AIChE Journal;2022-06-17

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