Pairing a Global Optimization Algorithm with EXAFS to Characterize Lanthanide Structure in Solution

Ensemble-average sampling of structures
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 nonrigid 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 and reduce the time required to identify
structures from generally a month to within a week. Additionally,
this approach is versatile and can be adapted to characterize main-group
metal complexes.