Alathea Davies

Academic Background

• 2025 - PhD, University of Wyoming, Physical Chemistry
• 2020 - BS, University of Idaho, Chemical Engineering

Professional Summary

Alathea “Ali” Davies is an Assistant Research Scientist at the University of Wyoming School of Computing with a focus on applied artificial intelligence and machine learning. Ali’s PhD work was in computational chemistry with a focus on understanding the structure, stability, and properties of porous materials from an atomistic perspective for applications ranging from liquid separations to thermoelectrics and semi-conductors. A majority of her research projects during this time were conducted in collaboration with experimental chemists; this experience has grounded her independent research career in the importance of enabling scientific discovery by collaborating within interdisciplinary teams.

Research Interests

• Applying, and developing novel, machine learning algorithms to materials science discovery, including structure and property prediction. This includes exploring algorithms that encode material structure into model architecture, such as in geometric deep learning algorithms like graph and topological neural networks.
• Deploying artificial intelligence tools for studying and optimizing critical mineral supply chains and industrial processes.
• Collaborating with domain experts to utilize artificial intelligence, machine learning, and data science to tackle challenging questions, especially those of significant relevance to the stakeholders within the state of Wyoming.

Google Scholar Profile – Link

ORCID – 0009-0009-4190-5555

Relevant Publications

• E. Davies, O. O. Adesina, I. M. Valdez, L. de Sousa Oliveira. “Multiple-Kernel Ridge Regression for Learning the Structure-Electronic Property Relationships of Pyranoazacoronene COFs.” Accepted with J. Chem. Theory Comput. https://doi.org/10.26434/chemrxiv.15007248/v1
• J. Wenzel, A. E. Davies, A. K. Goroncy, L. de Sousa Oliveira, J. O. Hoberg. “Covalent Organic Frameworks with 2-Fold Functional Pores Produced from a Monofunctional Monomer as Predicted by Computational Methods.” ACS Polymers Au 5(5) 2025. https://doi.org/10.1021/acspolymersau.5c00062
• Coe-Sessions, A. E. Davies, B. Dhokale, M. J. Wenzel, M. M. Gahrouei, N. Vlastos, J. Klaassen, B. A. Parkinson, L. de Sousa Oliveira, J. O. Hoberg. “Functionalized Graphene via a One-Pot Reaction Enabling Exact Pore Sizes, Modifiable Pore Functionalization, and Precision Doping.” J. Am. Chem. Soc. 146(48) 2024. https://doi.org/10.1021/jacs.4c10529
• E. Davies, M. J. Wenzel, C. L. Brugger, J. Johnson, B. A. Parkinson, J. O. Hoberg, L. de Sousa Oliveira. “Computationally Directed Manipulation of Cross-Linked Covalent Organic Frameworks for Membrane Applications.” Phys. Chem. Chem. Phys. 25(45) 2023. https://doi.org/10.1039/D3CP04452A
• A. Hosseini, A. E. Davies, I. Dickey, N. Neophytou, P. A. Greaney, L. de Sousa Oliveira. “Super-Suppression of Long Phonon Mean-Free-Paths in Nano-Engineering Si Due to Heat Current Anticorrelations.” Mater. Today Phys. 27, 2022. https://doi.org/10.1016/j.mtphys.2022.100719