Physical Sciences and Mathematics | Open Access Articles

Computational Investigation Of Mononuclear Iron Water Oxidation Catalyst Design, Kristal Stevens, Emily Jarvis Oct 2023

Computational Investigation Of Mononuclear Iron Water Oxidation Catalyst Design, Kristal Stevens, Emily Jarvis

Chemistry and Biochemistry Faculty Works

Hydrogen production from non-carbon sources is an essential component of clean and sustainable technology for reducing greenhouse gas emissions from fuels. Water oxidation, which splits water molecules into hydrogen (protons) and molecular oxygen, is a thermodynamically challenging, multistep reaction achieved in photosynthetic organisms via photocatalysis by the Oxygen Evolving Complex (OEC) of Photosystem II. Mononuclear water oxidation catalysts that aim to mimic nature typically rely on heavy, rare metals such as ruthenium and iridium. Replacing these metals with iron is particularly appealing because it is abundant, benign, and inexpensive. We use density functional theory to characterize the catalytic ability of …

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Quantum Chemistry–Machine Learning Approach For Predicting Properties Of Lewis Acid–Lewis Base Adducts, Hieu Huynh, Thomas J. Kelly, Linh Vu, Tung Hoang, Phuc An Nguyen, Tu C. Le, Emily Jarvis, Hung Phan May 2023

Quantum Chemistry–Machine Learning Approach For Predicting Properties Of Lewis Acid–Lewis Base Adducts, Hieu Huynh, Thomas J. Kelly, Linh Vu, Tung Hoang, Phuc An Nguyen, Tu C. Le, Emily Jarvis, Hung Phan

Chemistry and Biochemistry Faculty Works

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built …

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Powerpoint - Post-Planting Biodiversity Inventory, Center For Urban Resilience Mar 2023