Nicholas Hadler
I’m a chemistry PhD candidate in the Hartwig Group at the University of California, Berkeley, where I’m exploring how machine learning can accelerate discovery in synthetic chemistry.
My research brings together transition-metal catalysis, data science, and high-throughput experimentation to make catalysis faster, more predictive, and more data-driven. I’m especially interested in how domain knowledge can be used to make machine learning more effective for the natural sciences.
Selected Publications
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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)
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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
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Landscaper. A Python framework for constructing, quantifying, and visualizing deep-learning loss landscapes in both low and high dimensions. With efficient sampling strategies, a suite of TDA-based metrics, and interactive visualization tools, Landscaper provides an end-to-end workflow for uncovering geometric and topological structure in modern ML models.
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MolSelector. A lightweight web app for triaging molecular structures (.xyz, .mol, .mol2). Point it to a directory of files, inspect each molecule in an interactive 3D viewer powered by 3Dmol.js, and quickly tag it as accepted or rejected. Decisions are logged to a CSV file in the same folder for downstream analysis and version control.