Challenges Reconciling Theory and Experiments in the Prediction of Lattice Thermal Conductivity: The Case of Cu-Based Sulvanites-Reference-Cited by-同舟云学术

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Challenges Reconciling Theory and Experiments in the Prediction of Lattice Thermal Conductivity: The Case of Cu-Based Sulvanites

Published:2024-09-04 Issue: Volume: Page:
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Caro-Campos Irene¹,González-Barrios Marta María²^ORCID,Dura Oscar J.³^ORCID,Fransson Erik⁴^ORCID,Plata Jose J.¹^ORCID,Ávila David²^ORCID,Sanz Javier Fdez¹^ORCID,Prado-Gonjal Jesús²^ORCID,Márquez Antonio M.¹^ORCID

Affiliation:

1. Departamento de Química Física, Facultad de Química, Universidad de Sevilla, E-41012 Seville, Spain

2. Departamento de Química Inorgánica, Universidad Complutense de Madrid, E-28040 Madrid, Spain

3. Departamento de Física Aplicada, Universidad de Castilla-La Mancha, E-13071 Ciudad Real, Spain

4. Department of Physics, Chalmers University of Technology, SE-41296 Gothenburg, Sweden

Funder

Comunidad de Madrid

Ministerio de Ciencia, Innovaci?n y Universidades

European Commission

Red Espa?ola de Supercomputaci?n

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.4c01343

Reference57 articles.

1. Machine learning for molecular and materials science

2. Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

3. Data-Driven Strategies for Accelerated Materials Design

4. Scaling deep learning for materials discovery

5. Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods

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