PhD in Computational Modelling and Theoretical Chemistry, Data Scientist, and passionate about AI/ML Engineering

Welcome to my GitHub profile! I'm passionate about bridging the gap between computational chemistry, machine learning, and artificial intelligence to solve complex scientific problems.

• PhD in Computational Modelling and Theoretical Chemistry

  • Universitat de Barcelona (UB)
  • Research focus: Multi-scale modeling, molecular simulations, and computational chemistry

• Master's Degree in Advanced Chemistry

  • Universitat de Barcelona (UB)

• Bachelor's Degree in Chemistry

  • Universitat de Barcelona (UB)

I'm an AI/ML Engineer with a strong foundation in computational chemistry and theoretical modeling. My expertise lies in:

• Machine Learning & AI: Model development, neural networks, deep learning, feature engineering
• Computational Chemistry: Molecular dynamics, quantum mechanics simulations, multi-scale modeling
• Data Science: Data analysis, visualization, statistical modeling
• Scientific Computing: Python, numerical methods, scientific libraries
• Research & Development: Algorithm design, experimental validation, scientific publication

• Languages: Python, R, MATLAB, SQL
• ML Frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost
• Scientific Libraries: RDKit, ASE, GROMACS, Gaussian
• Data Tools: Pandas, NumPy, Matplotlib, Plotly
• Databases: PostgreSQL, MongoDB
• DevOps: Docker, Git, GitHub, Linux

• Machine Learning for Chemistry: Applying AI techniques to accelerate molecular discovery and property prediction
• Generative Models: Developing generative AI for novel molecular design
• Physics-Informed ML: Combining domain knowledge with machine learning for scientific discovery
• Drug Discovery: Leveraging computational methods and AI for pharmaceutical applications
• Materials Science: Predicting material properties using machine learning

• Molecular property prediction using neural networks
• Generative models for chemical compound design
• Classification and regression models for scientific data

• Multi-scale molecular simulations
• Quantum mechanical calculations
• Molecular dynamics simulations
• Structure-activity relationship (SAR) analysis

• Interactive dashboards and visualizations
• Statistical analysis of scientific datasets
• Machine learning model interpretability

• Collaborations on ML/AI projects in the scientific domain
• Opportunities to apply computational chemistry expertise to real-world problems
• Open-source contributions in machine learning and scientific computing
• Projects combining AI with chemistry, materials science, or biology

• 📧 Email: genisllm@gmail.com
• 💼 LinkedIn: Genis Lleopart
• 🔬 Google Scholar: Research Profile
• 📚 PhD Thesis: Available on UB Repository

This GitHub profile showcases my journey at the intersection of computational chemistry and machine learning. You'll find projects demonstrating:

• Modern ML/AI implementations
• Scientific computing applications
• Data science solutions
• Python best practices and clean code
• Open-source contributions

I believe in the power of combining rigorous scientific methodology with cutting-edge machine learning techniques to drive innovation.

Let's collaborate and create something amazing! Feel free to reach out. 🚀