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

• 🌱 I’m 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.
• 👯 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.
• 🔭 Prior to PhD, I have worked on bolstering HPAM polymer hydrodynamic size for high temperature & high salinity applications using molecular simulations.
• 🌱 Presently, I am working on the Machine Learning Operations Zoomcamp to enhance my skills in building and deploying machine learning models.
• 🔭 Looking forward, I hope to join an R&D position where I can focus on developing sustainable materials and technologies.
• 📫 You can to reach me on LinkedIn or Twitter.
• 💻 Here is the link to my Personal website.

📊 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