I am a ChemE PhD graduate from Romagnoli Group's at LSU utilizing the benefits of physics, data science and machine learning to enhance material design and operations of chemical systems.

     "Live as if you were to die tomorrow. Learn as if you were to live forever" 
                         - Mahatma Gandhi

• 🌱 I 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.
• Presently, I am learning news skills to enhance my data science skills. to be updated
• 👯 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.
• 🌱 Outside my PhD work, I am working on the MLOps & ML deployment 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
• Reinforcement Learning for separation: Developed RL agents to maximinze separation performance and energy efficiency. code
• 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
• BatteryInformatics: Leveraged cheminformatics tools (RDKit, Graph, LMM) to develop predictive models correlating electrolyte molecular structure with redox potential. 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: tolayiwola345-at-gmail.com or tolayi1-at-lsu.edu
• LinkedIn: Teslim Olayiwola
• Twitter: teslim404
• Google Scholar: Teslim Olayiwola
• Website: teslim404.com