Cross-Laboratory Validation of Machine Learning Models for Copper Nanocluster Synthesis Using Cloud-Based Automated Platforms

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This is a Quantitative Study study.

Ricardo Montoya-Gonzalez; R. Huerta; Martha Leticia Hernández Pichardo; Subha R. Das

doi:10.1039/d5dd00335k

January 1, 2025Digital DiscoveryOpen Access

Cross-Laboratory Validation of Machine Learning Models for Copper Nanocluster Synthesis Using Cloud-Based Automated Platforms

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Key Points

Synthesis was optimized using machine learning models, indicating improved reliability in material properties.
Key metrics highlighted the ability to validate models across different laboratories, enhancing reproducibility.
Assessment using cloud-based automated platforms facilitates real-time data processing for materials science applications.
The findings may enable faster advancements in material discovery, ultimately impacting various technological fields.

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Implication

Observational analysis confirmed machine learning models for synthesis in materials science, suggesting advancements in material discovery processes.

Abstract

The integration of machine learning (ML) into materials science has the potential to accelerate material discovery and optimize properties. However, the reliability of ML models depends heavily on the consistency...

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Cite This Study

Montoya-Gonzalez et al. (2025) studied this question.

synapsesocial.com/papers/6903fee5b25c631a42660035 https://doi.org/10.1039/d5dd00335k

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Cross-Laboratory Validation of Machine Learning Models for Copper Nanocluster Synthesis Using Cloud-Based Automated Platforms

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Cross-Laboratory Validation of Machine Learning Models for Copper Nanocluster Synthesis Using Cloud-Based Automated Platforms

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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