Computational Materials & Energy Scientist
Perovskites · DFT · Machine Learning
Atomistic degradation · devices · data-driven discovery
I'm Olanrewaju (Ola) Muili, a Computational Materials Scientist and DOE Marine Energy Fellow working at the intersection of energy materials, atomistic simulation, and machine learning. My current research focuses on metal halide perovskites, with an emphasis on understanding how degradation mechanisms emerge from defect chemistry and harsh environments.
Using first-principles density functional theory (DFT) with Quantum ESPRESSO, I study how oxygen interacts with iodide vacancies and other defects in MAPbI₃, mapping reaction pathways and their impact on electronic structure and device stability. This work is part of a Department of Defense–relevant project that examines how operating conditions and environmental stressors accelerate degradation in energy materials.
More broadly, I aim to connect atomistic mechanisms to device-level behavior and to use machine learning as a bridge between simulations, experiments, and system design. My goal is to help enable more durable perovskite-based photovoltaics and related optoelectronic devices for sustainable energy applications.
Connecting the dots. It's what I do.
I'm Olanrewaju (Ola) Muili, a Computational Materials Scientist and DOE Marine Energy Fellow working at the intersection of energy materials, atomistic simulation, and machine learning. My background spans an M.S. in Geosciences from Georgia State University, an M.S. in Computer Science from the University of Colorado Boulder, and a B.S. in Geology from the University of Ibadan, Nigeria. Across these experiences, I've been drawn to using quantitative tools to solve hard problems in the physical world.
These days my work is centered on metal halide perovskites and the question of why they degrade. I use first-principles density functional theory (DFT) with Quantum ESPRESSO to explore how oxygen and defects—especially iodide vacancies—reshape the local structure and electronic properties of MAPbI₃ and related materials. As part of a Department of Defense–relevant DOE Marine Energy project, I study how stresses such as moisture, temperature, and bias accelerate degradation, and how machine learning models built on top of simulations and experiments can help predict and ultimately mitigate these failure pathways.
Before focusing on perovskite energy materials, I worked as an Exploration Geologist, building data-driven and 3D geospatial models for mining and natural resource projects. That work, along with experience in machine learning and data mining, taught me how to move from complex data and models to solutions that deliver real impact. My goal now is to leverage this combined background in geoscience, computer science, and industry to tackle challenges in energy materials—especially perovskite stability and degradation—and to help enable more reliable and sustainable energy technologies.