Realizing the data-driven, computational discovery of metal-organic framework catalysts-Reference-Cited by-同舟云学术

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Realizing the data-driven, computational discovery of metal-organic framework catalysts

Published:2022-03 Issue: Volume:35 Page:100760
ISSN:2211-3398
Container-title:Current Opinion in Chemical Engineering
language:en
Short-container-title:Current Opinion in Chemical Engineering

Author:

Rosen Andrew S^ORCID,Notestein Justin M^ORCID,Snurr Randall Q^ORCID

Publisher

Elsevier BV

Subject

General Energy

Reference58 articles.

1. Metal-organic framework (MOF)-derived catalysts for fine chemical production;Konnerth;Coord Chem Rev,2020

2. Big-data science in porous materials: materials genomics and machine learning;Jablonka;Chem Rev,2020

3. Applications of machine learning in metal-organic frameworks;Chong;Coord Chem Rev,2020

4. Machine learning for atomic simulation and activity prediction in heterogeneous catalysis: current status and future;Ma;ACS Catal,2020

5. High-throughput screening of solid-state catalysts for nerve agent degradation;Palomba;Chem Commun,2018

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1. Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery;Coordination Chemistry Reviews;2024-09

2. Metal–Organic Frameworks as a Catalyst and Catalyst Support in Fuel Cells: From Challenges to Catalytic Application;Chemical Engineering & Technology;2024-08-28

3. Image and data mining in reticular chemistry powered by GPT-4V;Digital Discovery;2024

4. Progressive approach of porous coordination polymers toward catalysis and photocatalysis;Porous Coordination Polymers;2024

5. Machine learning in reaction engineering;Artificial Intelligence in Manufacturing;2024

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