Advancing Rare-Earth (4f) and Actinide (5f) Separation through Machine Learning and Automated High-Throughput Experiments

Augustine, Logan James; Wang, Yufei; Adelman, Sara Linnae; Batista, Enrique Ricardo; Kozimor, Stosh Anthony; Perez, Danny; Schrier, Joshua; Yang, Ping

doi:10.1021/acssuschemeng.4c06166

Title: Advancing Rare-Earth (4f) and Actinide (5f) Separation through Machine Learning and Automated High-Throughput Experiments

Journal Article · 28 October 2024 · ACS Sustainable Chemistry & Engineering

DOI: https://doi.org/10.1021/acssuschemeng.4c06166 · OSTI ID:2475442

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Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Fordham University, The Bronx, NY (United States)

Identifying improved and sustainable alternatives to “classic” separation techniques is an active research field due to its potential widespread impact in fundamental and applied chemistry. As basic purification methodologies, like liquid–liquid extraction, undergo continuous refinement by chemists and engineers, identifying new conditions that outperform existing techniques can be difficult. A major contributor to this challenging problem is the need to explore a vast experimental space to identify the precise conditions that optimize the separation procedure. The advent of artificial intelligence and the advancement of robotic technologies offer the potential to shift the traditional design paradigm. Toward that end, we applied a combination of Bayesian Optimization and high-throughput robotic experiments on the liquid–liquid extraction of thorium (Th⁴⁺) and demonstrated that this approach speeds up discovery and significantly accelerates the optimization process. By using Bayesian Optimization as a guide, our automated instrument carried out a total of 339 distribution ratio measurements, corresponding to 113 unique conditions, identifying the optimal experimental conditions with reduced experimental efforts by an estimated 74% compared to a traditional full screening approach. This time and cost saving is particularly significant for radioactive materials, as it not only is more economical and sustainable but also minimizes human exposure to radioactivity.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: National Energy Research Scientific Computing Center (NERSC); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2475442

Report Number(s):: LA-UR--24-31710

Journal Information:: ACS Sustainable Chemistry & Engineering, Journal Name: ACS Sustainable Chemistry & Engineering Journal Issue: 45 Vol. 12; ISSN 2168-0485

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Title: Advancing Rare-Earth (4f) and Actinide (5f) Separation through Machine Learning and Automated High-Throughput Experiments

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