24 citations · 25 across the 2 of their papers we have counts for
3 papers · 1 filter
Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes
Ralf Meyer, Daniel Benjamin Kasman Chu, Heather J. Kulik
Graph-based machine learning models for materials properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simple…
Ligand additivity relationships enable efficient exploration of transition metal chemical space
Naveen Arunachalam, Stefan Gugler, Michael G. Taylor +7
To accelerate exploration of chemical space, it is necessary to identify the compounds that will provide the most additional information or value. A large-scale analysis of mononuc…
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy
Chenru Duan, Daniel B. K. Chu, Aditya Nandy +1
Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high throughpu…