Machine learning anomaly detection of automated HPLC experiments in the cloud laboratory
Autonomous experiments are vulnerable to unforeseen adverse events. We developed a transferable ML framework that flags affected HPLC runs in real time and provides expert-level quality control without human oversight.
Authors
- Filipp Gusev (ORCID: https://orcid.org/0000-0002-1167-345X)
- Olexandr Isayev (ORCID: https://orcid.org/0000-0001-7581-8497)
- Anqin Xu
- Ben J. Smith (ORCID: https://orcid.org/0000-0002-6227-5566)
- R. P. Quinn (ORCID: https://orcid.org/0000-0002-0879-6045)
- Benjamin C Kline
- Brian Frezza
Institutions
- Wells Fargo (United States) (US)
- St. Cloud Hospital (US)
- Carnegie Mellon University (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00253b
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- National Centre for Supercomputing Applications
- San Diego Supercomputer Center
- Office of Advanced Cyberinfrastructure
- Division of Chemistry