Computational Chemist · Scientific Software Developer

Computational chemist and scientific software developer with 6+ years of experience building scalable molecular simulation and data-driven workflows.

My work sits at the intersection of:

• quantum chemistry (QM/MM)
• machine learning for molecular systems
• high-performance scientific software (C++/Fortran/Python)

I focus on developing robust, reproducible pipelines that connect atomistic simulations with data-driven models, with applications in molecular and materials modelling.

Currently a senior postdoctoral researcher at the Scuola Normale Superiore (Pisa), working in collaboration with the developers of the Amsterdam Modeling Suite (SCM).

• GEOM (Python)
  AI-driven tools for molecular design and dataset generation, integrating cheminformatics workflows with simulation-ready structures
  → https://github.com/pgrobasillobre/geom

• FretLab (C++/Fortran)
  Simulation framework for electronic energy transfer in complex molecular environments
  → https://github.com/pgrobasillobre/FretLab-Cpp

• PlasmonX (Fortran/Python)
  Open-source code for multiscale nanoplasmonics simulations
  → https://github.com/plasmonX/plasmonX

• Industry collaboration with developers of the Amsterdam Modeling Suite (SCM), contributing to QM/MM methodologies and production-level code

Programming
C++, Fortran, Python, Bash

Scientific & ML stack
RDKit, PyTorch, TensorFlow, NumPy, SciPy, Pandas

Core expertise
QM/MM simulations · molecular modelling · scientific computing · HPC workflows · data pipelines

• Machine learning models for molecular property prediction (GNN-based approaches)
• Integration of classical simulation workflows with quantum computing methods
• Scalable data generation and automation on HPC systems

• LinkedIn: https://www.linkedin.com/in/grobas-illobre
• Website: https://www.pgrobasillobre.com