Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides-Reference-Cited by-同舟云学术

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Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides

Published:2024-04-25 Issue:17 Volume:15 Page:4721-4728
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Barra Julian¹^ORCID,Chahal Rajni²^ORCID,Audesse Simone¹,Zhang Jize³,Zhong Yu³,Kabel Joey⁴,Lam Stephen¹^ORCID

Affiliation:

1. Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States

2. Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States

3. Mechanical and Materials Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States

4. Electrified Thermal Solutions, Inc., Medford, Massachusetts 02155, United States

Funder

National Science Foundation

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.4c00506

Reference47 articles.

1. Design Strategies for Efficient Nonstoichiometric Mixed Metal Oxide Electrocatalysts: Correlating Measurable Oxide Properties to Electrocatalytic Performance

2. High-Efficiency Perovskite Solar Cells

3. High-temperature thermochemical energy storage based on redox reactions using Co-Fe and Mn-Fe mixed metal oxides

4. Basis for Fluoride Salt–Cooled High-Temperature Reactors with Nuclear Air-Brayton Combined Cycles and Firebrick Resistance-Heated Energy Storage

5. Storage Requirements and Costs of Shaping Renewable Energy Toward Grid Decarbonization

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