The Clayborne Research Group employs a range of computational techniques to drive innovation in materials discovery and design. Our work is inherently multidisciplinary, integrating concepts from chemistry, physics, chemical engineering, and materials science to tackle complex scientific challenges. Through close collaboration with researchers across diverse fields, we aim to address pressing issues facing society. Our core research areas include the understanding the chemical and physical properties of molecules and clusters, modeling electrochemical processes, applying quantum computing to problems in chemistry, and leveraging artificial intelligence to accelerate the discovery of molecular materials.
Current Research Projects
Chemical and Physical Properties of Molecules and Clusters
Understanding the chemical and physical properties of molecules and clusters is important to gain insight into how matter behaves and may be used in materials. We explore the structure, bonding, reactivity, and energetics of molecular systems and clusters that range in size and composition using computational methods. Through computational investigations, we aim to uncover fundamental principles that govern transformations and interactions in these systems leading to advances in (nano)technology, environmental remediation, and materials design.
Recent Publications:
Modeling Electrochemical Processes
Electrochemical processes are key in a variety of industrial, biological, environmental, and technological fields. Being able to predict and design materials that can outperform previous materials such as surfaces is critical for advancing technology. At the core of developing future materials, one must understand not only the reaction networks, but also the role of morphology and electronic structure. We are interested in understanding the mechanistic details of electrochemical processes using a series of molecules, surfaces, and nanoparticles.
Quantum Computing for Chemistry
Quantum computing offers a transformative approach to solving complex problems in chemistry and material science by using quantum mechanical principles to simulation molecules and materials with better accuracy and in less time. We are using quantum algorithms such as the Variational Quantum Eigensolver (VQE) in conjunction with quantum embedding techniques to probe energies and properties of molecules, clusters, and materials.
Recent Publication: Exploring Quantum Computing for Metal Cluster Analysis, N. Pollard, A.C. Hines, A.Z. Clayborne, J. Phys. Chem. A (2025)
Artificial Intelligence for Molecular Materials Development
Determining structure and properties using ab initio methods is a mainstay in chemistry. Often researchers need to understand the electron response using time-intensive quantum theories and computational techniques beyond traditional DFT approaches to design molecular materials. We are using machine learning to accelerate the discovery of organic molecules with precise electron response properties for batteries and quantum materials.