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NabKh/README.md

👋 Hi, I'm Nabil Khossossi (Nab)

Typing SVG

🔬 Postdoctoral Researcher | AI-Driven Materials Discovery | Computational Chemistry

Website Scholar LinkedIn ORCID

🔬 About Me

I'm a computational materials scientist at the Dutch Institute for Fundamental Energy Research (DIFFER), working at the intersection of quantum chemistry, machine learning, and autonomous materials discovery. My research focuses on developing physics-informed AI frameworks for accelerating the discovery of next-generation energy materials.

Current Research:

  • 🤖 AI4Mat Self-driving Laboratory for autonomous materials optimization
  • ⚡ Physics-informed ML for high-efficiency photovoltaic materials
  • 🧪 Multi-objective catalyst discovery using active learning

🤝 Let's Collaborate!

I'm actively seeking collaborations in:

  • 🔬 AI-driven materials discovery platforms
  • 🌱 Sustainable energy materials (solar cells, catalysts)
  • 🤖 Self-driving laboratories and autonomous experimentation
  • 📊 Large-scale materials databases and informatics

Looking for: Assistant Professor positions in computational materials science, ML for chemistry, and device acceleration platforms.

📧 Email: [n.khossossi@differ.nl & n.khossossi@hotmail.com]
🌐 Website: sustai-nabil.com
💼 Open to: Group leader/Senior Scientists | Faculty positions | Research collaborations | Consulting

"Bridging quantum mechanics, machine learning, and materials innovation"

⭐ Star my repositories if you find them useful!

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  1. OER-Free-Energy-GPAW OER-Free-Energy-GPAW Public

    This repository contains a Python script designed to perform Density Functional Theory (DFT) calculations to obtain the Oxygen Evolution Reaction (OER) free energy. The calculations are conducted u…

    Python 1

  2. SACs_HER_2024 SACs_HER_2024 Public template

    Python scripts for Few shot machine learning model to predict HER

    Python 1

  3. ASCICat ASCICat Public

    A unified multi-objective framework for translating computational catalyst data into reproducible, experimentally-actionable rankings

    Jupyter Notebook

  4. ML-for-Materials-Science ML-for-Materials-Science Public

    A comprehensive, interactive learning path for applying machine learning to materials discovery, property prediction, and atomistic simulations.

    Jupyter Notebook

  5. MXene-Structure-Generator MXene-Structure-Generator Public

    A Python framework for systematic generation and manipulation of MXene (M_{n+1}X_nT_z) crystal structures for computational materials science.

    Python

  6. Physics-Informed-ML-Solar-Cells Physics-Informed-ML-Solar-Cells Public

    A comprehensive tutorial and framework for applying physics-informed machine learning to photovoltaic materials discovery using Materials Project data.

    Jupyter Notebook