Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands

no code implementations • 5 May 2022 • Chenru Duan, Adriana J. Ladera, Julian C. -L. Liu, Michael G. Taylor, Isuru R. Ariyarathna, Heather J. Kulik

We compute MR diagnostics for over 5, 000 ligands present in previously synthesized transition metal complexes in the Cambridge Structural Database (CSD).

Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning

no code implementations • 29 Jul 2021 • Michael G. Taylor, Aditya Nandy, Connie C. Lu, Heather J. Kulik

Focusing on oxidation potentials, we obtain a set of 28 experimentally characterized complexes to develop a multiple linear regression model.

BIG-bench Machine Learning regression

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles

no code implementations • 24 Jun 2021 • Chenru Duan, Shuxin Chen, Michael G. Taylor, Fang Liu, Heather J. Kulik

Computational virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery.

Feature Importance