Applying graph neural network models to molecular property prediction using high-quality experimental data

Qu, Chen; Schneider, Barry I.; Kearsley, Anthony J.; Keyrouz, Walid; Allison, Thomas C.

doi:10.1016/j.aichem.2024.100050

Artificial Intelligence Chemistry

2024

DOI: 10.1016/j.aichem.2024.100050

|View full text |Cite

Applying graph neural network models to molecular property prediction using high-quality experimental data

Chen Qu,

Barry I. Schneider,

Anthony J. Kearsley

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience

Krishnamoorthy,

Balasubramani

2024

Advcd Theory and Sims

View full text Add to dashboard Cite

Artificial Intelligence (AI) is pivotal in advancing science, including nanomaterial studies. This review explores AI‐based image processing in nanoscience, focusing on algorithms to enhance characterization results from instruments like scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, atomic force microscopy etc. It addresses the significance of AI in nanoscience, challenges in advancing AI‐based image processing for nano material characterization, and AI's role in structural analysis, property prediction, deriving structure‐property relations, dataset augmentation, and improving model robustness. Key AI techniques such as Graph Neural Networks, adversarial training, transfer learning, generative models, attention mechanisms, and federated learning are highlighted for their contributions to nano science studies. The review concludes by outlining persisting challenges and thrust areas for future research, aiming to propel nanoscience with AI. This comprehensive analysis underscores the importance of AI‐powered image processing in nanomaterial characterization, offering valuable insights for researchers.

show abstract

Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience

Krishnamoorthy,

Balasubramani

2024

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

A graph convolutional network approach for hyperspectral image analysis of blueberries physiological traits under drought stress

Rahman,

Busby,

Hanif

et al. 2025

Smart Agricultural Technology

View full text Add to dashboard Cite

Automation and Machine Learning for Accelerated Polymer Characterization and Development: Past, Potential, and a Path Forward

Beaucage,

Sutherland,

Martin

2024

Macromolecules

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Applying graph neural network models to molecular property prediction using high-quality experimental data

Cited by 3 publications

References 65 publications

Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience

Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience

A graph convolutional network approach for hyperspectral image analysis of blueberries physiological traits under drought stress

Automation and Machine Learning for Accelerated Polymer Characterization and Development: Past, Potential, and a Path Forward

Contact Info

Product

Resources

About