I am a Chemical Engineering PhD candidate at LSU in Romagnoli Group utilizing the benefits of physics & machine learning to enhance the design of electrochemical systems.
"Live as if you were to die tomorrow. Learn as if you were to live forever"
- Mahatma Gandhi
- š± Iām aspire to gain expertise in material and process simulations at all scales from atomistic to numerical and in applied machine learning to solve material science problems.
- šÆ In my PhD, I worked on bridging knowledge from physics-based (e.g. molecular simulation and numerical modeling) and data-driven models for material and process simulation of electrochemical systems such as electrodialysis, electrodeionization, capactive deionization and CO2 electrolyzers. Also, I have worked on applying molecular simulation and artificial intelligence to accelerate molecular design.
- š Prior to PhD, I have worked on bolstering HPAM polymer hydrodynamic size for high temperature & high salinity applications using molecular simulations.
- š± Presently, I am working on the Machine Learning Operations Zoomcamp to enhance my skills in building and deploying machine learning models.
- š Looking forward, I hope to join an R&D position where I can focus on developing sustainable materials and technologies.
- š« You can to reach me on LinkedIn or Twitter.
- š» Here is the link to my Personal website.
š My Stats:
- Chemical Modeling with Physics-based and Data-driven approach
- Computational Molecular design
- Material & Process Optimization
- Molecular Simulation of Materials
- Machine Learned Force Field (MLFF) development
- Languages: Python, MATLAB
- Machine learning: Scikit-Learn, TensorFlow, Keras, PyTorch, MLflow, Docker, Streamlit, PySpark, Terraform
- Chemical Eng. & Chemistry: Aspen Plus, GROMACS, LAMMPS, Gaussian, Rdkit, Deep Graph Library (dgl).
- Platforms: Linux, Git
- Soft Skills: Research, Leadership, Event Management
- Proficiency in the use of Microsoft Office Power Point, Word, Excel, and JMP
- Synthesis & characterization: Nanocrystals synthesis, catalyst synthesis, X-ray diffraction (XRD), Diffuse Reflectance IR Fourier Transform Spectroscopy (DRIFTS), UV etching and Design of Experiment.
- Bridging Physics and Data-Driven methods: Developed numerical model and machine learning (ML) model to perform optimization studies for two common electrochemical systems (electrodialysis and electrodeionization). code
- Transfer Learning for missing data: assess the possibility of resolving missing data with transfer learning. code
- Feature Embedding: Combined information from experiment, molecular structure and molecular simulation with machine learning to enhance predictive modeling of membrane properties. code
- Generative Molecular Design: Combined generative AI, predictive modeling, reinforcement learning and MD simulation to create molecules with desired properties. code
- Machine learning for accelerated electrochemical reduction: Leverage machine learning and optimization to design new experimental conditions with enhanced C2+ production. code
- Physicis Informed Machine learning: Developing PINN and Neural ODE to resolve limitations of physics ODEs in capturing selective ion separation in electrodialysis. In preparation
- Failure detection in pumps: Participated in BPX hackathon and developed a LSTM-based data-driven model to estimate ESP run life. Ranked 3rd out of 30 submissions and received the Implementation award for code reproducibility. code
- Active Learning modeling: Developed codes to train active learning models based on different query strategies. Presently testing the methods on problems such as protein adsorption, structure-property modelling, & electrochemical separation performance. code
- Transformer: Trained transformer to encode sequence and classify with PyTorch, & HuggingFace. code 1 & code 2
- KNN guided molecular design: Developing a molecular design optimization framework integrating k-Nearest Neighbour and Genetic Algorithms. code
- Facial Recognition: Collaborated on the development of software utilizing Mediapipe + Blender framework to track facial structure and emotion classification via a trained CNN-based classifier. code
- Piano Music Generation: Trained two deep learning LSTM models as 1) critic of good or bad music and 2) composer to generate new music. Tools: Python, PyTorch, Scikit-Learn. code
- Facial Recognition: Collaborated on the development of software utilizing Mediapipe + Blender framework to track facial structure and emotion classification via a trained CNN-based classifier. code
- Tox24 challenge: Predict the in vitro activity of compounds from chemical structure. Code
- LLM for Water Purification: Collaborated on applying prompt engineering to develop chatbots that guide researchers to the optimal water treatment solution for specific cases, based on contaminant composition, cost, and resource availability.
- Email: tolayi1-at-lsu.edu
- LinkedIn: Teslim Olayiwola
- Twitter: teslim404
- Google Scholar: Teslim Olayiwola
- Website: teslim404.com