Data-Driven Kinetic Reaction Networks for Separation Chemistry

Journal Article · Tue May 13 00:00:00 UTC 2025 · Journal of Chemical Theory and Computation

DOI: https://doi.org/10.1021/acs.jctc.4c01783 · OSTI ID:2566455

Lee, Jiyoung ^[1]; Augustine, Logan J.  ^[2]; Henkelman, Graeme  ^[3]; Yang, Ping  ^[2]; Perez, Danny  ^[2]

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1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); University of Texas, Austin, TX (United States)
2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
3. University of Texas, Austin, TX (United States)

Understanding complex, multistep chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. Here, in this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid–liquid extraction mechanism of uranium using N,N-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models that are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO₂(NO₃)₂(DEHiBA)₂, agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at a low computational cost.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
• Grant/Contract Number: 89233218CNA000001; AC07-05ID14517
• OSTI ID: 2566455
• Report Number(s): LA-UR--24-32487
• Journal Information: Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 10 Vol. 21; ISSN 1549-9618
• Publisher: American Chemical SocietyCopyright Statement
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
density functional theory
extraction
kinetic modeling
uranium