Autonomous continuous flow reactor synthesis for scalable atom-precision

Bobby G. Sumpter, Kunlun Hong, Rama K. Vasudevan, Ilia Ivanov, Rigoberto Advincula

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

With new instrumentation design, robotics, and in-operando hyphenated analytical tool automation, the intelligent discovery of synthesis pathways is becoming feasible. It can potentially bridge the gap for the scale-up of new materials. We review current progress and describe a new system that uses an autonomous continuous flow chemistry framework to translate high-quality lead molecules and materials to quantities that can meet scalability demands. At the core is a continuous flow synthesis platform that can design its viable synthesis pathway to a particular molecule or material and then autonomously carry it out. This is realized by integrating: (1) A workflow/architecture for multimode chemical/materials characterization in-line. The in-line characterization modes are NMR, ESR, IR, Raman, UV-Vis, GC-MS, and HPLC, along with ex-situ modes for X-Ray and neutron scattering; (2) Integration for feedback/analysis/data storage of the control variables; (3) A core software stack that includes deep learning and reinforcement learning alongside quantum chemistry and molecular dynamics; (4) On-demand compute architectures that parse calculations to compute resources needed which include light-weight edge, mid-level edge (NVIDA DGX-2), and high-performance computing. We demonstrate preliminary results on how this autonomous reactor system can enhance our ability to deliver deuterated materials, copolymers, and site-substituted molecules.

Original languageEnglish
Article number100234
JournalCarbon Trends
Volume10
DOIs
StatePublished - Mar 2023

Funding

Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This work was performed at the Center for Nanophase Materials Sciences (CNMS), a US Department of Energy Office of Science User Facilility at Oak Ridge National Laboratory (ORNL). The development of the autonomous flow reactor system was supported by the INTERSECT Initiative as part of the Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725.

Keywords

  • Autonomous synthesis
  • Flow chemistry
  • Machine learning
  • Polymers

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