Nicholas Hadler

I’m a chemistry PhD candidate in the Hartwig Group at the University of California, Berkeley.

My research focuses on combining transition-metal catalysis with machine learning, data science, and high-throughput experimentation to accelerate discovery in synthetic chemistry. More broadly, I’m interested in how we can leverage domain knowledge to address the challenges of applying machine learning to the natural sciences.

Selected Publications

• 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. ChemRxiv, 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.

Selected Projects

• Landscaper. A Python framework that constructs, quantifies, and visualizes deep-learning loss landscapes in both low and high dimensions. Equipped with efficient sampling strategies, a novel TDA-based metrics, and interactive plotting utilities, the library offers an end-to-end toolkit for uncovering geometric and topological insights to better understand complex ML models.