Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials-Reference-Cited by-同舟云学术

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Published:2021 Issue:27 Volume:12 Page:9309-9317
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Lo Dico Giulia¹²³⁴⁵^ORCID,Nuñez Álvaro Peña⁶³^ORCID,Carcelén Verónica⁶³,Haranczyk Maciej¹²³^ORCID

Affiliation:

1. IMDEA Materials Institute

2. 28906 Getafe

3. Spain

4. Department of Materials Science and Engineering and Chemical Engineering

5. Universidad Carlos III de Madrid

6. Tolsa Group

Abstract

Machine learning models of the morphology and surface activity of natural clays enable multiobjective design of functional materials, e.g. acid nanocatalyzers.

Funder

Comunidad de Madrid

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SC/D1SC00816A

Reference51 articles.

1. What Are the Best Materials To Separate a Xenon/Krypton Mixture?

2. Hierarchical porous materials: catalytic applications

3. Porous materials in catalysis: challenges for mesoporous materials

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