Rate My Professor Andrew Tarzia

Tarzia utilizes high-throughput computational chemistry and physics-based models to predict the structures and supramolecular properties of self-assembled cages. His research interests encompass developing algorithms to efficiently explore self-assembly structural outcomes from experimental inputs, implementing high-throughput methodologies for predicting supramolecular properties to design catalysts and sensors, and creating automated multiscale workflows to optimize computational decision-making. He prioritizes robust open-source software that bridges experimental and computational workflows, thereby minimizing materials discovery costs and broadening access to advanced computational methods. Notable publications include "Systematic exploration of accessible topologies of cage molecules via minimalistic models" (Chemical Science, 2023, with Wolpert, Jelfs, and Pavan), "A Combined Experimental and Computational Exploration of Heteroleptic cis-Pd₂L₂L'₂ Coordination Cages through Geometric Complementarity" (Chemistry: A European Journal, 2025), "Unlocking the computational design of metal-organic cages" (Chemical Communications, 2022), "High-throughput computational evaluation of low symmetry Pd₂L₄ cages to aid in system design" (Angewandte Chemie International Edition, 2021), and "Diastereoselective Self-Assembly of Low-Symmetry PdₙL₂ₙ Nanocages through Coordination-Sphere Engineering" (Angewandte Chemie International Edition, 2023). His contributions relate to UN Sustainable Development Goals 7 (Affordable and Clean Energy) and 13 (Climate Action).