Virginia Carnevali

EPFL SB ISIC LCBC
BCH 4122 (Batochime UNIL)
Av. François-Alphonse Forel 3
1015 Lausanne

Web site:  Web site:  https://lcbc.epfl.ch

EPFL SB ISIC LCBC
BCH 4122 (Batochime UNIL)
Av. François-Alphonse Forel 3
1015 Lausanne

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Administrative data

Fields of expertise

Theoretical condensed matter physics: theory and simulations of structural, electronic, magnetic and optical properties (Quantum ESPRESSO), ab-initio molecular dynamics (CP2K), and Kinetic Monte Carlo codes; automate Quantum ESPRESSO workflows and high-throughput calculations (AFLOWπ); generation of tight-binding hamiltonians, transport properties, and data analysis (PAOFLOW).

Materials for photovoltaic: Pb- and Sn-based organic perovskites; investigation and improvement of the HTL-ETL interaction and contact via organic/inorganic molecules.

2-dimensional materials:
electronic and structural properties of graphene on metallic surfaces; development of mathematical models for the identification of simulation’s cells in 2D stacked materials systems and 2D materials over substrates.

Thermoelectric materials: investigation and prediction of electronic and lattice transport properties for high-performing thermoelectric materials with a particular focus on the aikinite (PbCuBiS3) e kuramite (Cu3SnS4) families.

Topological materials: topological materials originated by translational symmetry breaking (HgTe-CdTe junctions); Weyl semimetals with time-reversal symmetry breaking (SrM1−xZnxSb alloys); properties of single-crystal kagomé metal CoSn.

Quantum computing: development and application of optimization methods for routing and condensed matter problems on D-Wave quantum annealers. Development of new methods for condensed matter stability problems, order-disorder transitions on quantum annealers.


















Teaching & PhD

Courses

Advanced simulations of solar cell devices

State-of-the-art solar cells. Quantum and classical simulation techniques applied to perovskite solar cells (software: CP2K, QE, LAMMPS,GAUSSIAN). Parametrization of interatomic machine learning potentials using ab initio simulations.