Pablo Grobas Illobre

Pablo Grobas Illobre, PhD

Computational chemist and scientific software developer with over six years of experience in quantum chemistry, QM/MM methodologies, and high-performance computing. Lead developer of quantum-chemistry algorithms (C++/Fortran) within the Amsterdam Modeling Suite, in collaboration with Software for Chemistry & Materials (SCM). Skilled in Python for data analysis, cheminformatics (RDKit), and machine learning (TensorFlow, PyTorch).

Technical Skills

  • Expertise: QM/MM and molecular modelling
  • Programming: Python, C++, Fortran, MATLAB
  • Libraries: RDKit, TensorFlow, PyTorch, scikit-learn, NumPy, SciPy
  • Development: Git, VS Code, CI/CD basics
  • Systems: Linux, Bash, HPC environments
  • Scientific Software: Amsterdam Modeling Suite, Gaussian, GAMESS

Experience & Training

Senior Postdoctoral Researcher,

Scuola Normale Superiore (Pisa, Italy)

02/2025 – Present

I lead the development of QM/MM quantum-chemistry algorithms in C++ and Fortran within the Amsterdam Modeling Suite, in collaboration with the Software for Chemistry & Materials. This work is complemented with Python for machine learning, data analysis, and scientific visualization in spectroscopy and multiscale modelling research.

My research integrates intensive software development to investigate:

  • QM/MM Surface-Enhanced Spectroscopies: fluorescence, Raman scattering, Raman optical activity.
  • Plasmonic Materials: graphene & metal nanoparticles.
  • Plasmon-Mediated Electronic Energy Transfer.
Working languages English · Italian

Research Projects

SCM (Software for Chemistry & Materials) logo

Development of Quantum Chemistry Software for SCM (Software for Chemistry & Materials)

Industry collaboration associated with Scuola Normale Superiore | 2020 – Present
  • Algorithms for Surface-Enhanced Raman Scattering (SERS).
  • Pipeline for Surface-Enhanced Fluorescence (SEF).
  • Tools for plasmon-mediated resonance energy transfer.
  • Implementation of Surface-Enhanced Raman Optical Activity (SEROA) calculations.
plasmonX logo

Development of High Performance Software for Nanoplasmonics Research

Scuola Normale Superiore | 2020 – Present
  • Standalone, parallelized implementations for ωFQFμ and BEM equations.
  • High-performance algorithms for solving the linear systems via direct inversion and iterative schemes.
  • Analysis of the optical response (absorption spectra, charge/density distributions, and more).
  • User-friendly interface via integration with the GEOM module.
FARE logo

FARE — “Framework per l’attrazione e il rafforzamento delle eccellenze per la ricerca in Italia”

Scuola Normale Superiore | 2020 – Present | PI: Chiara Cappelli

  • Python programming (in-house codes) for data analysis, manipulation, and visualization (e.g., Python-driven figures in P. Grobas Illobre et al., Nanoscale Adv., 2024, 6, 3410 ).
  • Quantum chemistry QM/MM software development in C++ and Fortran (AMS + in-house) for surface-enhanced fluorescence and plasmon-mediated electronic energy transfer.
  • Intensive use of HPC infrastructures to automate and streamline data production workflows.
GEMS logo

GEMS — General Embedding Models for Spectroscopy ,

ERC Consolidator Grant

Scuola Normale Superiore | 2020 – 2025 | PI: Chiara Cappelli

  • Python programming (in-house codes) for data analysis, manipulation, and visualization (e.g., Python-driven figures in T. Giovannini et al., ACS Photonics, 2022, 9, 3025 ).
  • Currently developing a machine learning pipeline in Python to study and simulate graphene samples.
  • Quantum chemistry software development in Fortran (Amsterdam Modeling Suite + in-house) for plasmonic materials, QM/MM SERS, and Raman optical activity.
  • Intensive use of HPC infrastructures to streamline large-scale data production workflows.
See related softwares →

Selected Publications

  • Computer Physics Communications (2026)

    T. Giovannini , P. Grobas Illobre , P. Lafiosca , L. Nicoli , L. Bonatti , S. Corni C. Cappelli

    We present plasmonX, an open-source code for simulating the plasmonic response of nanostructures. It combines high-performance implementations of the atomistic frequency-dependent fluctuating charges and dipoles approach (ωFQFμ) and the continuum Boundary Element Method (BEM), enabling accurate modeling of metallic and graphene-based systems and in-depth analysis of their optical properties.

    Fortran · Python · HPC · PlasmonX
    Read the article →

  • Small Structures (2026)

    P. Grobas Illobre , G. Conter , L. Bonatti , T. Giovannini , A. Fortunelli , C. Cappelli

    We combine large-scale atomistic amorphous carbon models generated with DynReaxMas and optical simulations based on the ωFQ method to uncover how disorder, curing, and local morphology shape plasmonic hot spots. The approach identifies four recurrent classes of field-enhancement sites—dangling bonds, stacked graphene-like sheets, carbon chains, and atomistic defects—and shows that cured structures can produce stronger and more localized enhancements, with values comparable to defective metallic nanojunctions.

    Fortran · Python · HPC · PlasmonX
    Read the article →
See all publications →

Awards

  • PhD awarded with honors (with honors).
  • Scuola Normale Superiore PhD Scholarship (2020–2025).
  • Two NanoX Research Scholarships (2019 & 2020).
  • Erasmus Internship Fellowship (2019–2020).
  • Consejo Superior de Investigaciones Científicas (CSIC) – Jae Intro Fellowship (2019).
  • Extraordinary Prize for achieving the best academic record in the Bachelor of Chemistry (2018).
  • Erasmus+ scholarship (2016).