In Silico Evolution of High-Performing Metal Organic Frameworks for Methane Adsorption-Reference-Cited by-同舟云学术

In Silico Evolution of High-Performing Metal Organic Frameworks for Methane Adsorption

Published:2021-07-15 Issue:7 Volume:61 Page:3232-3239
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Beauregard Nicole¹,Pardakhti Maryam¹²³^ORCID,Srivastava Ranjan¹⁴^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, University of Connecticut, 191 Auditorium Rd. Unit 3222, Storrs, Connecticut 06269, United States

2. Department of Computer Science & Engineering, University of Connecticut, Storrs, Connecticut 06269, United States

3. Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, United States

4. Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States

Funder

General Electric

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c01479

Reference40 articles.

1. Impact of Shale Gas Development on Regional Water Quality

2. A comparative study of the performance and exhaust emissions of a spark ignition engine fuelled by natural gas and gasoline

3. Life-Cycle Greenhouse Gas Emissions of Shale Gas, Natural Gas, Coal, and Petroleum

4. The Predictive Content of Energy Futures: An Update on Petroleum, Natural Gas, Heating Oil and Gasoline

Cited by 11 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. Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data;Journal of Computational Physics;2024-10

2. Data Driven Discovery of MOFs for Hydrogen Gas Adsorption;Journal of Chemical Theory and Computation;2023-09-27

3. Analyzing of metal organic frameworks performance in CH4 adsorption using machine learning techniques: A GBRT model based on small training dataset;Journal of Environmental Chemical Engineering;2023-06

4. Machine Learning for Predicting Gas Adsorption Capacities of Metal Organic Framework;Machine Learning and Deep Learning in Computational Toxicology;2023

5. Physics-Informed Genetic Programming for Discovery of Partial Differential Equations from Scarce and Noisy Data;2023