Emmanuel Olugbade

As a seasoned Engineer with over 10 years of experience, I specialize in computational materials science, specifically battery modeling and AI-driven materials discovery. My core strength lies in conducting continuum scale, DFT and MD simulations to analyze materials and system performance such as charge transport, and ionic conductivity. My work has led to significant advancements in the field, including the development of phase-field models that simulate dendrite formation and mechanical failure mechanisms.

In addition to this, I've used my expertise in machine learning to build models that predict defect formation energies, diffusion pathways, and electrochemical stability. This has resulted in an improved prediction accuracy of between 15% - 25%. By integrating electrostatic potential data with these ML models, we've been able to enhance ionic conductivity forecasting by 20%.

I also have a proven track record in high-performance computing and workflow automation. For instance, I've designed Python-based automation workflows for VASP, LAMMPS, and Quantum ESPRESSO that have reduced computational setup time by 40%.

My unique approach bridges computational insights with experimental validation to drive real-world advancements in battery materials. I've collaborated with experimental teams to validate computational predictions using various methods such as Electrochemical Impedance Spectroscopy (EIS), Equivalent Circuit Modeling (ECM), and microstructure characterization.

If you're looking for someone who combines deep technical knowledge with a passion for innovation and problem-solving, let's connect. I'm always open to exploring opportunities where I can apply my skills to make a tangible impact on the future of energy storage solutions.