Organometallic actinide compounds are critical for applications ranging from nuclear waste treatment to rare earth separations. Due to the relative complexity in synthesis and electronic structure of organometallic actinide compounds compared to organic compounds, atomistic-level and experimental data for actinide complexes has lagged organic chemistry. Here, we leverage mononuclear organometallic compounds chemical structures derived from the Cambridge Structure Database (CSD) to develop machine-learning predictors targeted towards determining binding sites on ligands and metal-center geometries for actinide chemistries. In combination with generalized 3D structure-generation routines using recent accelerated ab-initio methods, we enable accurate ab-initio screening of large swaths of underexplored actinide chemistries. In total, we present tools for high-throughput computational exploration of actinide-ligand chemical space towards enhanced actinide separation chemistries.

Learning actinide-ligand binding motifs and structure from organometallic structural databases

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.