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

Rizvi Syed Aal E Ali¹, Jiaolong Meng¹, Xuefeng Jiang^{1

2

3}

Affiliations

¹ Hainan Institute of East China Normal University, State Key Laboratory of Petroleum Molecular & Process Engineering, Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, China.
² School of Chemistry and Chemical Engineering, Henan Normal University, Xinxiang, Henan, 453007, China.
³ State Key Laboratory of Organometallic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai, 200032, China.

PMID: 40874323
DOI: 10.1002/asia.202500825

Review

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

Rizvi Syed Aal E Ali et al. Chem Asian J. 2025 Oct.

. 2025 Oct;20(20):e00825.

doi: 10.1002/asia.202500825. Epub 2025 Aug 28.

Authors

Rizvi Syed Aal E Ali¹, Jiaolong Meng¹, Xuefeng Jiang^{1

2

3}

Affiliations

¹ Hainan Institute of East China Normal University, State Key Laboratory of Petroleum Molecular & Process Engineering, Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, China.
² School of Chemistry and Chemical Engineering, Henan Normal University, Xinxiang, Henan, 453007, China.
³ State Key Laboratory of Organometallic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai, 200032, China.

PMID: 40874323
DOI: 10.1002/asia.202500825

Abstract

The integration of machine learning (ML) and high-throughput experimentation (HTE) is rapidly transforming research practices in synthetic chemistry. Traditional trial-and-error methods, historically slow and labour-intensive, are being replaced by automated, predictive workflows that significantly accelerate the optimization of chemical reactions. This review highlights the foundational principles and recent advancements in ML and HTE, while emphasizing automation, parallelization, and miniaturization across different systems and their adaptation in autonomous laboratories. Case studies illustrate successful application of ML and HTE in synthetic chemistry, underscoring the enhanced efficiency, and precision through this synergy. The review concludes by addressing current challenges and future directions, outlining how ongoing developments in automation, robotics, and AI/ML-driven experimentation will shape the future landscape of chemistry research.

Keywords: AI‐assisted synthesis; Automation; High throughput experimentation; Machine learning.

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References

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

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

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