Accelerating Materials Discovery for Polymer Solar Cells: Data-Driven Insights Enabled by Natural Language Processing-Reference-Cited by-同舟云学术

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Accelerating Materials Discovery for Polymer Solar Cells: Data-Driven Insights Enabled by Natural Language Processing

Published:2024-08-06 Issue: Volume: Page:
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Shetty Pranav¹^ORCID,Adeboye Aishat²,Gupta Sonakshi³,Zhang Chao¹,Ramprasad Rampi³^ORCID

Affiliation:

1. School of Computational Science & Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, Georgia 30332, United States

2. School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, Georgia 30332, United States

3. School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, Georgia 30332, United States

Funder

Office of Naval Research

JPMorgan Chase and Company

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.4c00709

Reference81 articles.

1. Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond

2. Rational design and synthesis of polythioureas as capacitor dielectrics

3. Dielectric Polymers Tolerant to Electric Field and Temperature Extremes: Integration of Phenomenology, Informatics, and Experimental Validation

4. Computationally accelerated discovery of functional and structural Heusler materials

5. Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials

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1. Borides as promising M₂AX phase materials with high elastic modulus using machine learning and optimization;Journal of Materials Informatics;2024-08-27

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