Pairing a Global Optimization Algorithm with EXAFS to Accelerate Prediction of Lanthanide Structures in Solution

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Abstract
Ensemble-average sampling of structures sampled from ab initio molecular dynamics (AIMD) simulations can be used to predict theoretical extended X-ray absorption fine structure (EXAFS) signals that closely match experimental spectra. However, AIMD simulations are time-consuming and resource-intensive, particularly for solvated lanthanide ions, which often form multiple non-rigid geometries with high coordination numbers. To accelerate the characterization of lanthanide structures in solution, we employed the Northwest Potential Energy Surface Search Engine (NWPEsSe), an adaptive-learning global optimization algorithm, to efficiently screen first-shell structures. As case studies, we examine two systems: Eu(NO3)3 dissolved in acetonitrile with a terpyridine ligand (terpyNO2), and Nd(NO3)3 dissolved in acetonitrile. The theoretical spectra for structures identified by NWPEsSe were compared to both experimental and AIMD-derived EXAFS spectra. The NWPEsSe algorithm successfully identified the proper solvation structure for both Eu(NO3)3(terpyNO2) and Nd(NO3)(acetonitrile)3, with the calculated EXAFS signals closely matching the experimental spectra for the Eu-ligand complex and showing good similarity for the Nd salt; the better agreement with the ligand-containing structure is attributed to a less dynamic coordination environment due to the rigid ligand. The key advantage of the global optimization algorithm lies in its ability to sample the coordination environment across the potential energy surface. The structure identification process can be done with a variety of methods and generally has reduced the time required from a month to within a week.

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