Publications

A more complete list can be found on Google Scholar.

Peer-reviewed articles

• Lambert, W.*; Felten, S.*; Hadler, N.; Rinehart, N. I.; Swiatowiec, R.; Storer, G.; Henle, J.; Servos, M.; Yang, C.; Bay, A.; Eyimegwu, P.; Shekhar, S.; Hartwig, J. Unleashing the Power of Potassium 2-Ethylhexanoate as a Mild and Soluble Base for Pd-Catalyzed C–N Cross-Coupling. JACS, 2025. (*equal contribution)

  

• Yuan, T.; Tang, Q.; Shan, C.; Ye, X.; Wang, J.; Zhao, P.; Wojtas, L.; Hadler, N.; Chen, H.; Shi, X. Alkyne Trifunctionalization via Divergent Gold Catalysis: Combining π-Acid Activation, Vinyl-Gold Addition, and Redox Catalysis. JACS, 2021.

  

Preprints

• Chen, J.*; Hadler, N.*; Xie, T.; Hnatyshyn, R.; Geniesse, C.; Yang, Y.; Mahoney, M. W.; Perciano, T.; Hartwig, J. F.; Maciejewski, R.; Weber, G. H. Landscaper: Understanding Loss Landscapes Through Multi-Dimensional Topological Analysis. arXiv, 2026. (*equal contribution)

  

• Hadler, N.; Rinehart, N. I.; Elkin, M.; Nicolai, J.; Gheibi, G.; Chen, J.; Avaylon, M.; Maciejewski, R.; Weber, G. H.; Mahoney, M. W.; Perciano, T.; Hartwig, J. F. A 3D, Structure-Based, Deep Learning Approach for Predicting the Regioselectivity of Transition-Metal Catalysis. ChemRxiv, 2026.

  

Conference abstracts

• Hadler, N., A 3D, Structure-Based, Deep Learning Approach for Predicting the Regioselectivity of Olefin Hydroformylation, Oral Presentation at the 8th Artificial Intelligence in Chemistry Symposium, Cambridge, London, UK, September 2025.
• Hadler, N., Predicting the Regioselectivity of Olefin Hydroformylation with Machine Learning, Invited Poster Presentation: “Unlocking a New Era: How AI is Transforming Drug Discovery and Development Symposium”, ACS Spring, 2025.
• Hadler, N., Predicting the Regioselectivity of Olefin Hydroformylation with Machine Learning, Bay Area Chemistry Symposium, 2024.