Predictive machine learning models trained on experimental datasets for electrochemical nitrogen reduction-Reference-Cited by-同舟云学术

Predictive machine learning models trained on experimental datasets for electrochemical nitrogen reduction

Published:2024 Issue: Volume: Page:
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Rosser Darik A.¹²,Farris Brianna R.¹²,Leonard Kevin C.¹²^ORCID

Affiliation:

1. Center for Environmentally Beneficial Catalysis, The University of Kansas, Lawrence, KS, USA

2. Department of Chemical & Petroleum Engineering, The University of Kansas, Lawrence, KS, USA

Abstract

Obtaining useful insights from machine learning models trained on experimental datasets collected across different groups to improve the sustainability of chemical processes can be challenging due to the small size and heterogeneity of the dataset.

Funder

Army Research Office

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D3DD00151B

Reference25 articles.

1. Machine learning: Trends, perspectives, and prospects

2. Estimation of catalytic performance by neural network — product distribution in oxidative dehydrogenation of ethylbenzene

3. Using Artificial Intelligence To Forecast Water Oxidation Catalysts

4. Can artificial neural networks help the experimentation in catalysis?

5. A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Cited by 1 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. Design of Single‐Atom Catalysts on C₅N₂ for Nitrogen Fixation at Ambient Conditions: A First‐Principles Study;Chemistry – A European Journal;2024-07-23