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This is a Literature Review study.

September 5, 2025Chemistry - An Asian Journal

Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis

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Authors

RARizvi Syed Aal E AliEast China Normal University JMJiaolong MengHainan Normal University XJXuefeng JiangLiaoning Technical University

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Implication

This review highlights the integration of machine learning and high-throughput experimentation, emphasizing automation and efficiency in synthetic chemistry.

Key Points

The integration of machine learning and high-throughput experimentation significantly accelerates the optimization of chemical reactions.
Case studies illustrate enhanced efficiency and precision in synthetic chemistry through machine learning and high-throughput experimentation techniques.
Automation, parallelization, and miniaturization are essential principles driving the transformation of research practices in synthetic chemistry.
The review addresses challenges and future directions in AI/ML-driven experimentation, suggesting ongoing innovation will shape chemistry research.

Cite This Study

Ali et al. (2025) studied this question.

synapsesocial.com/papers/68bb3d5b2b87ece8dc956258 https://doi.org/10.1002/asia.202500825

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Implication

This review highlights the integration of machine learning and high-throughput experimentation, emphasizing automation and efficiency in synthetic chemistry.

Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis

Discussion

Member takes

Implication

Key Points

Cite This Study

Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis

Discussion

Member takes

Implication

Key Points

Cite This Study

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