Overall Mission
Embedded Files
Catalysis is a cornerstone of the global economy, fueling over 35% of GDP and powering more than 80% of manufactured goods. Yet, the intricate nature of catalytic reactions demands a detailed, molecular-level understanding to unlock truly efficient and sustainable processes. This is where computational chemistry becomes indispensable.
In the Ogba Reaction Mechanisms Group, we leverage advanced computational techniques, including density functional theory (DFT), data analytics, cheminformatics, and machine learning, to conduct detailed mechanistic analyses of chemical reactions.
Our mission is to create explanatory models and design strategies that precisely guide the implementation of catalysts in chemical reaction mechanisms. We're not just studying reactions; we're providing the foundational insights needed to optimize chemical processes, reduce waste, and accelerate the development of materials.
Sustainable Catalysis with Main Group Elements
Overview
We are investigating the catalytic potential of Lewis acidic and Lewis basic catalysts from main group elements, specifically uncovering substrate activation modes and chemical origins of catalytic turnover. The goal is to develop rules for improving improving catalytic turnover and designing new reactions employing these catalysts. We're actively exploring the potential of main group catalysts in sulfur-fluoride exchange reactions and heteroallene reductions.
Broader Goals
We aim to develop theoretical frameworks that enable the replacement of conventional, often expensive, and environmentally costly transition metal-based catalysts. Our focus on main group elements offers a path toward more cost-effective and environmentally benign alternatives for industrial and academic applications alike. These works have been supported by the National Institutes of Health and the American Chemical Society.
Unlocking Mechanistic Insights using Data Science
Overview
We're also interested in the intersection of chemistry and data science. Our mission is to deepen our understanding of chemical reaction mechanisms by harnessing the power of data analytics, cheminformatics, and cutting-edge machine learning.
Our team has developed custom Python programs designed to extract and analyze critical geometric and electronic molecular descriptors. By correlating these intricate details with molecular function, we're building bridges between data and discovery.
Broader Goals
Our aim is to develop reliable automation tools for structure-activity relationship (SAR) studies in the small-molecule chemical reactivity domain. We are committed to ensuring these tools are firmly grounded in chemical principles, generate chemically meaningful results, and are readily accessible to chemists, supporting a new wave of innovation powered by automation and machine learning.
Page updated
Google Sites
Report abuse