Dissertation topics suggested by the teacher.
- Machine Learning for Materials: on-the-fly Machine Learning Force Fields (MD), Bayesian optimization (Potential Energy surfaces), Neural Networks, Generative ML algorithms. Development and application to different area of materials science (charge dynamics, hydrogen diffusion, defects in solids, energy materials)
- High-throughput calculations of optical and magnetic properties
- Diagrammatic QuantumMonte Carlo: prediction of unconventional non-collinear spin ordering using first principles methods, many body techniques and Machine Learning
- Correlated and spin-orbit coupled 2D magnets
- Diagrammatic Quantum Monte Carlo : fundamentals, code development,ML-augmented DiagMC
- Electron-phonon interaction: polaron physics, anharmonic effect in using first principles methods and machine learning force field: bulk and surfaces
- Computational Surface Science and computational single atom catalysis
Recent dissertations supervised by the teacher.
First cycle degree programmes dissertations
- Analisi delle proprietà termiche dei solidi cristallini con la teoria dei fononi armonici e anarmonici
- Calcoli a primi principi per l'assorbimento di idrogeno su grafene arricchito da adatomi metallici
- Calcolo a primi principi dell’energia di gap
della perovskite CH3NH3PbI3 per
applicazioni a celle solari
- Calcolo del coefficiente di diffusione dell'idrogeno nel magnesio mediante algoritmi di machine learning
- Calcolo della Struttura a Bande del Silicio
- Electronic and Optical Properties of Lead Titanate from Beyond-DFT Methods
- La superconduttività dalla teoria BCS tradizionale al caso dei cuprati con annessa risoluzione numerica dell’equazione di gap in condizione di interazione anisotropa
- Modellizzazione di polaroni in DFT: TiO2 Rutilo
- Solving the many-body quantum problem with artificial neural networks
- Studio ab initio delle proprietà dinamiche di reticolo di semiconduttori
Second cycle degree programmes dissertations
- Comparison between ARPES and first principles band structure of BaPbO3
- Enhancing Diagrammatic Monte Carlo via machine learning
- Magnetic properties of 2D Chromium trihalides by first-principles calculations
- Predicting the quasiparticle energies and bandgaps of bulk materials with machine learning techniques
