Abstract
Optimization of separations for selective binding of rare earth elements and actinides is critical to guarantee a supply of materials essential to needs including national security and defense, technology, medicine, and communications. Computational modeling is key to accelerate solutions for selective separations; however, tools to predict accurate representations of the physical systems of interest require high performance computing resources to be developed to facilitate robust modeling. This study evaluates computational molecular modeling and machine learning applications on property analysis relevant to the binding of lanthanum and actinium with carboxylic acids. It focuses on assessing accuracy of computational predictions based on benchmark computational results due to lack of experimental information. Properties evaluated include Gibbs free energies of reaction, relativistic effects, diagnostics, partial charges, structural characteristics, and coordination sphere and equivalent volume.
| Original language | English |
|---|---|
| Pages (from-to) | 5469-5485 |
| Number of pages | 17 |
| Journal | Journal of Radioanalytical and Nuclear Chemistry |
| Volume | 331 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2022 |
Funding
The authors gratefully acknowledge Prof. George Schweitzer, Dr. Rose Boll, Dr. Howard Hall, Dr. John D. Auxier II, and Jana Starks for useful discussions. Research by DAP, EFV, and RJH was supported by the US Department of Energy, Office of Science, via Award DE-SC0022327. Part of this work used computational and storage services associated with the Hoffman2 Shared Cluster provided by UCLA Institute for Digital Research and Education’s Research Technology Group. A portion of the computation for this work was performed on the computational resources at the Infrastructure for Scientific Applications and Advanced Computing (ISAAC) supported by the University of Tennessee. Partial computational resources were provided by UNT's High Performance Computing Services, a division of the University Information Technology with additional support from UNT Office of Research and Economic Development. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562 [68]. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper. http://www.tacc.utexas.edu The authors gratefully acknowledge Prof. George Schweitzer, Dr. Rose Boll, Dr. Howard Hall, Dr. John D. Auxier II, and Jana Starks for useful discussions. Research by DAP, EFV, and RJH was supported by the US Department of Energy, Office of Science, via Award DE-SC0022327. Part of this work used computational and storage services associated with the Hoffman2 Shared Cluster provided by UCLA Institute for Digital Research and Education’s Research Technology Group. A portion of the computation for this work was performed on the computational resources at the Infrastructure for Scientific Applications and Advanced Computing (ISAAC) supported by the University of Tennessee. Partial computational resources were provided by UNT's High Performance Computing Services, a division of the University Information Technology with additional support from UNT Office of Research and Economic Development. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562 [68]. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper. http://www.tacc.utexas.edu
Keywords
- Actinium
- Binding
- Carboxylic acids
- Computational
- Lanthanum
- Machine learning
- Separations