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PreprintCC BYChemRxiv

MoSeS Enables Autonomous Chromatography Operation through Programmable Analytical Expertise

  • Mohammad Haddadnia1,2,3,4,5
  • Malcolm Sim6
  • Han Hao1,2,5,7
  • Kazuhiro Hotta1,2,5,8
  • Martin Seifrid1,2,5,9,10
  • Dennis Fedotov11,12
  • Tony C. Wu1,2,5
  • Shi Xuan Leong13,14
  • Felix Strieth-Kalthoff11,12
  • Alán Aspuru-Guzik1,2,4,5,7,9,15,16,17
17 affiliations
  1. 1Department of Chemistry
  2. 2University of Toronto
  3. 3Harvard University
  4. 4Vector Institute for Artificial Intelligence
  5. 5Department of Computer Science
  6. 6Yale School of Medicine
  7. 7Acceleration Consortium
  8. 8Mitsubishi Chemical Corporation Science & Innovation Center
  9. 9Department of Materials Science and Engineering
  10. 10North Carolina State University
  11. 11School of Mathematics and Natural Sciences
  12. 12Bergische Universität Wuppertal
  13. 13School of Chemistry, Chemical Engineering and Biotechnology
  14. 14Nanyang Technological University
  15. 15NVIDIA
  16. 16Department of Chemical Engineering and Applied Chemistry
  17. 17Canadian Institute for Advanced Research (CIFAR)
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Abstract

Chromatography–mass spectrometry is a central tool for chemical and biological analysis across a wide range of applications. However, its use in automated workflows has remained limited because instrument operation, chromatogram interpretation and method development have often relied on experienced users. Here, we translate key elements of this analytical expertise into programmable operations, and apply them across workflows of increasing analytical complexity. Most importantly, we show that autonomous, machine-learning-based chromatographic method development can be accelerated substantially by moving beyond black-box optimization and incorporating analytical expertise. We further demonstrate practical use cases from synthetic chemistry and materials discovery, including the automated annotation of complex reaction mixtures, and the autonomous development of a purification method for a target compound from a crude reaction mixture. These capabilities are provided through MoSeS, an open-source Python framework that integrates sample-by-sample instrument control, analyte-resolved data processing, cross-run analyte tracking, and physicochemical retention modelling within a common software layer. Together, these results show how analytical operations that are commonly performed by experts can be formalized into reusable computational workflows. This work provides both a usable software toolbox and practical demonstrations of integrating chromatography–mass spectrometry systems into automated laboratory routines – ranging from custom data analysis pipelines to AI-guided decision making in self-driving laboratories.

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Cite

BibTeXRIS
BibTeX
@misc{haddadnia2026moses,
  title = {{MoSeS Enables Autonomous Chromatography Operation through Programmable Analytical Expertise}},
  author = {Mohammad Haddadnia and Malcolm Sim and Han Hao and Kazuhiro Hotta and Martin Seifrid and Dennis Fedotov and Tony C. Wu and Shi Xuan Leong and Felix Strieth-Kalthoff and Alán Aspuru-Guzik},
  howpublished = {ChemRxiv},
  year = {2026},
  month = aug,
  doi = {10.26434/chemrxiv.15008042/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008042/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - MoSeS Enables Autonomous Chromatography Operation through Programmable Analytical Expertise
AU  - Mohammad Haddadnia
AU  - Malcolm Sim
AU  - Han Hao
AU  - Kazuhiro Hotta
AU  - Martin Seifrid
AU  - Dennis Fedotov
AU  - Tony C. Wu
AU  - Shi Xuan Leong
AU  - Felix Strieth-Kalthoff
AU  - Alán Aspuru-Guzik
PY  - 2026
DA  - 2026/08/30
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15008042/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008042/v1
AB  - Chromatography–mass spectrometry is a central tool for chemical and biological analysis across a wide range of applications. However, its use in automated workflows has remained limited because instrument operation, chromatogram interpretation and method development have often relied on experienced users. Here, we translate key elements of this analytical expertise into programmable operations, and apply them across workflows of increasing analytical complexity. Most importantly, we show that autonomous, machine-learning-based chromatographic method development can be accelerated substantially by moving beyond black-box optimization and incorporating analytical expertise. We further demonstrate practical use cases from synthetic chemistry and materials discovery, including the automated annotation of complex reaction mixtures, and the autonomous development of a purification method for a target compound from a crude reaction mixture. These capabilities are provided through MoSeS, an open-source Python framework that integrates sample-by-sample instrument control, analyte-resolved data processing, cross-run analyte tracking, and physicochemical retention modelling within a common software layer. Together, these results show how analytical operations that are commonly performed by experts can be formalized into reusable computational workflows. This work provides both a usable software toolbox and practical demonstrations of integrating chromatography–mass spectrometry systems into automated laboratory routines – ranging from custom data analysis pipelines to AI-guided decision making in self-driving laboratories.
ER  -