3 papers
physics.chem-ph2026
ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table
Jacob W. Toney, Samir Darouich, Yiran Wang +3
Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined t…
physics.chem-ph2026
The BOS-TMC Dataset: DFT Properties of 159k Experimentally Characterized Transition Metal Complexes Spanning Multiple Charge and Spin States
Aaron G. Garrison, Jacob W. Toney, Tatiana Nikolaeva +3
We present the Boston Open-Shell Transition Metal Complex (BOS-TMC) dataset, a set of density functional theory (DFT) properties for 159k experimentally characterized mononuclear t…
physics.chem-ph2026
The BOS-Lig Dataset: Accurate Ligand Charges from a Consensus Approach for 66,810 Experimentally Synthesized Ligands
Roland G. St. Michel, Ryan J. Jang, Aaron G. Garrison +2
Understanding ligand properties is essential for computational high-throughput screening of transition metal complexes. However, ligand properties such as net charge and other info…