Riccardo Dal Molin

I have always been drawn to the meeting point between the rigour and clarity of theoretical physics and the pragmatism of experimental physics, and this is what got me hooked on computational physics from my very first years of study.

Among these computer “experiments”, which use theory to produce results that can be compared directly with measurements, solid-state physics is for me the most fascinating branch and the richest in applications. This led me into the deep world of ab initio calculations, of high-throughput automation for the discovery of new materials, and of machine learning for the development, prediction and design of new materials.

Interests

• Ab initio electronic structure

  Density-functional theory calculations of materials properties.

• Code design, automation and optimization

  High-throughput workflows for materials discovery, built on the AiiDA ecosystem.

• Neural networks for materials modelling

  Equivariant graph neural networks, machine-learned interatomic potentials and total-energy models.

• Molecular dynamics

  Ab initio and classical simulations of materials and interfaces.

• Geometry and topology of electronic structure

  Wannier functions, Berry phases and electric polarization.

In preparation

• A universal diffusion model for the quantum position operator
• Automated all-functional piezoelectric tensor without supercell

Theses and reports

1. 2024

   Drag properties of graphene coatings: atomistic simulations

   Master’s thesis, University of Trieste. Advisors: M. Peressi, E. Smargiassi, D. Bidoggia.

2. 2022

   Development of an interatomic potential based on deep neural networks for single layers of tungsten ditelluride

   Bachelor’s thesis, University of Trieste. Advisor: A. Marrazzo.

Posters

1. 2026

   Equivariant machine learning of Berry-phase polarization

   MARVEL-ICTP College: Materials simulations in the age of AI, Trieste.

2. 2025

   Slip properties of graphene–water interfaces: atomistic simulations

   International Workshop on Computational Physics and Materials Science: Total Energy and Force Methods.

3. 2022

   Development of a DeepMD potential for monolayer WTe₂

   Young Researchers’ Workshop on Machine Learning for Materials.

Tutorials

1. 2026

   Berry-phase electric polarization with Quantum ESPRESSO

   Tutor at the MARVEL-ICTP College: Materials simulations in the age of AI.

   GitHub
2. 2026

   Automated active learning of a neural-network interatomic potential with AiiDA-TrainsPot

   Tutor at the Summer School on Molecular Dynamics for Materials Science, Nanotechnology, and Biophysics.

1. PhD in Theory and Numerical Simulation of Condensed Matter

   SISSA, Trieste. 2024 to present, in collaboration with Eni S.p.A.

2. Master’s degree in Physics, 110/110 cum laude

   University of Trieste, 2022 to 2024

3. Bachelor’s degree in Physics

   University of Trieste, 2018 to 2022. Includes an internship in optical spectroscopy at Elettra Sincrotrone Trieste.