Dr Verdi's research is in the field of computational materials physics. Her work employs first-principles or ab initio methods, complemented by machine learning techniques, to predict and understand physical properties of materials without relying on empirical models.
She received her doctorate in Materials from the University of Oxford in 2017. After working at the University of Oxford and the University of Vienna, Dr Verdi moved to the University of Sydney in 2023 as an ARC DECRA Fellow. In the same year she then joined UQ as a Lecturer in Condensed Matter Physics.
Her current research focuses on understanding the structural, optical and thermodynamic properties of atomic defects for applications in quantum technologies. She is also interested in studying the influence of atomic vibrations, defects, temperature and disorder on the intrinsic properties of various functional materials that can be exploited for novel technologies. Feel free to reach out to Dr Verdi if you are interested in simulating materials properties from first principles using supercomputers and exploring how this can help develop better materials.
Journal Article: Hard antiphase domain boundaries in strontium titanate: A comparison of Landau-Ginzburg-Devonshire and ab initio results
Tröster, A., Pils, J., Bruckner, F., Rychetsky, I., Verdi, C. and Schranz, W. (2023). Hard antiphase domain boundaries in strontium titanate: A comparison of Landau-Ginzburg-Devonshire and ab initio results. Physical Review B, 108 (14) 144108. doi: 10.1103/physrevb.108.144108
Journal Article: Machine Learning Density Functionals from the Random-Phase Approximation
Riemelmoser, Stefan, Verdi, Carla, Kaltak, Merzuk and Kresse, Georg (2023). Machine Learning Density Functionals from the Random-Phase Approximation. Journal of Chemical Theory and Computation, 19 (20), 7287-7299. doi: 10.1021/acs.jctc.3c00848
Journal Article: Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential
Jinnouchi, Ryosuke, Minami, Saori, Karsai, Ferenc, Verdi, Carla and Kresse, Georg (2023). Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential. Journal of Physical Chemistry Letters, 14 (14), 3581-3588. doi: 10.1021/acs.jpclett.3c00293
First-principles design of atomic defects for quantum technologies
(2023–2026) ARC Discovery Early Career Researcher Award
First principles calculations of defects in solids for quantum technologies
Doctor Philosophy
New Methods for Strongly Correlated Electrons in Chemically Complex Materials
Doctor Philosophy
Thermodynamic properties of atomic defects for quantum technologies
Atomic defects in solids are one of the most promising single-photon sources or 'quantum emitters', an important building block for many quantum technologies. In order to design and engineer better quantum emitters, a fundamental understanding of their optical and electronic properties, as well as defect formation and migration, is essential. In this project, first-principles quantum mechanical calculations combined with machine-learning techniques are used in order to uncover key properties such as defect dynamics, formation mechanisms, free energies and stabilities at room and elevated temperatures. The theoretical insights gained in the project aim to inform the design of atomic defects systems for tailored applications as quantum emitters. The student will gain experience with high-performance computing and materials simulation methods, in particular first-principles methods and machine-learned potentials.
Atomistic modelling of solid surfaces and 2D structures
Density functional theory (DFT) is a prominent tool that enables the simulation of materials and molecules at the atomic scale 'from first principles', i.e., without relying on empirical data. To underscore its importance in modern materials physics and beyond, it should suffice to mention that 12 papers on the top-100 list of the most-cited papers of all time, including 2 of the top 10, are all related to DFT. In this project, first-principles DFT calculations will be used to investigate and characterise the structural and electronic properties of 2D structures and solid surfaces. These properties can be directly compared to experimental data, such as scanning tunneling microscopy (STM) experiments conducted in SMP. Target systems include solvated molecules on alkali halide structures, perovskite materials for next-gen solar cells, and oxide structures on metal superconductors.
The student will gain experience with widely used first-principles materials modelling software and high-performance computing.
Caruso, Fabio, Verdi, Carla and Giustino, Feliciano (2020). Many-body calculations of plasmon and phonon satellites in angle-resolved photoelectron spectra using the cumulant expansion approach. Handbook of materials modeling: methods: theory and modeling. (pp. 341-365) edited by Wanda Andreoni and Sidney Yip. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-44677-6_2
Tröster, A., Pils, J., Bruckner, F., Rychetsky, I., Verdi, C. and Schranz, W. (2023). Hard antiphase domain boundaries in strontium titanate: A comparison of Landau-Ginzburg-Devonshire and ab initio results. Physical Review B, 108 (14) 144108. doi: 10.1103/physrevb.108.144108
Machine Learning Density Functionals from the Random-Phase Approximation
Riemelmoser, Stefan, Verdi, Carla, Kaltak, Merzuk and Kresse, Georg (2023). Machine Learning Density Functionals from the Random-Phase Approximation. Journal of Chemical Theory and Computation, 19 (20), 7287-7299. doi: 10.1021/acs.jctc.3c00848
Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential
Jinnouchi, Ryosuke, Minami, Saori, Karsai, Ferenc, Verdi, Carla and Kresse, Georg (2023). Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential. Journal of Physical Chemistry Letters, 14 (14), 3581-3588. doi: 10.1021/acs.jpclett.3c00293
Ranalli, Luigi, Verdi, Carla, Monacelli, Lorenzo, Kresse, Georg, Calandra, Matteo and Franchini, Cesare (2023). Temperature-dependent anharmonic phonons in quantum paraelectric KTaO3 by first principles and machine-learned force fields. Advanced Quantum Technologies, 6 (4) 2200131, 1-7. doi: 10.1002/qute.202200131
Verdi, Carla, Ranalli, Luigi, Franchini, Cesare and Kresse, Georg (2023). Quantum paraelectricity and structural phase transitions in strontium titanate beyond density functional theory. Physical Review Materials, 7 (3) L030801. doi: 10.1103/PhysRevMaterials.7.L030801
Liu, Peitao, Wang, Jiantao, Avargues, Noah, Verdi, Carla, Singraber, Andreas, Karsai, Ferenc, Chen, Xing-Qiu and Kresse, Georg (2023). Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111). Physical Review Letters, 130 (7) 078001. doi: 10.1103/PhysRevLett.130.078001
Hard antiphase domain boundaries in strontium titanate unravelled using machine-learned force fields
Tröster, A., Verdi, C., Dellago, C., Rychetsky, I., Kresse, G. and Schranz, W. (2022). Hard antiphase domain boundaries in strontium titanate unravelled using machine-learned force fields. Physical Review Materials, 6 (9) 094408. doi: 10.1103/PhysRevMaterials.6.094408
Engel, Manuel, Miranda, Henrique, Chaput, Laurent, Togo, Atsushi, Verdi, Carla, Marsman, Martijn and Kresse, Georg (2022). Zero-point renormalization of the band gap of semiconductors and insulators using the projector augmented wave method. Physical Review B, 106 (9) 094316. doi: 10.1103/PhysRevB.106.094316
Kandolf, Nikolaus, Verdi, Carla and Giustino, Feliciano (2022). Many-body Green's function approaches to the doped Fröhlich solid: Exact solutions and anomalous mass enhancement. Physical Review B, 105 (8) 085148. doi: 10.1103/PhysRevB.105.085148
Phase transitions of zirconia: Machine-learned force fields beyond density functional theory
Liu, Peitao, Verdi, Carla, Karsai, Ferenc and Kresse, Georg (2022). Phase transitions of zirconia: Machine-learned force fields beyond density functional theory. Physical Review B, 105 (6) L060102. doi: 10.1103/PhysRevB.105.L060102
Verdi, Carla, Karsai, Ferenc, Liu, Peitao, Jinnouchi, Ryosuke and Kresse, Georg (2021). Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials. npj Computational Materials, 7 (1) 156. doi: 10.1038/s41524-021-00630-5
α-β phase transition of zirconium predicted by on-the-fly machine-learned force field
Liu, Peitao, Verdi, Carla, Karsai, Ferenc and Kresse, Georg (2021). α-β phase transition of zirconium predicted by on-the-fly machine-learned force field. Physical Review Materials, 5 (5) 053804. doi: 10.1103/PhysRevMaterials.5.053804
First-principles hydration free energies of oxygenated species at water-platinum interfaces
Jinnouchi, Ryosuke, Karsai, Ferenc, Verdi, Carla and Kresse, Georg (2021). First-principles hydration free energies of oxygenated species at water-platinum interfaces. Journal of Chemical Physics, 154 (9) 094107. doi: 10.1063/5.0036097
Electron-polaron dichotomy of charge carriers in perovskite oxides
Husanu, M. A., Vistoli, L., Verdi, C., Sander, A., Garcia, V., Rault, J., Bisti, F., Lev, L. L., Schmitt, T., Giustino, F., Mishchenko, A. S., Bibes, M. and Strocov, V. N. (2020). Electron-polaron dichotomy of charge carriers in perovskite oxides. Communications Physics, 3 (1) 62. doi: 10.1038/s42005-020-0330-6
Jinnouchi, Ryosuke, Karsai, Ferenc, Verdi, Carla, Asahi, Ryoji and Kresse, Georg (2020). Descriptors representing two-and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials. Journal of Chemical Physics, 152 (23) 234102. doi: 10.1063/5.0009491
Ab initio theory of polarons: Formalism and applications
Sio, Weng Hong, Verdi, Carla, Poncé, Samuel and Giustino, Feliciano (2019). Ab initio theory of polarons: Formalism and applications. Physical Review B, 99 (23) 235139. doi: 10.1103/PhysRevB.99.235139
Polarons from First Principles, without Supercells
Sio, Weng Hong, Verdi, Carla, Poncé, Samuel and Giustino, Feliciano (2019). Polarons from First Principles, without Supercells. Physical Review Letters, 122 (24) 246403. doi: 10.1103/PhysRevLett.122.246403
Davies, Christopher L., Filip, Marina R., Patel, Jay B., Crothers, Timothy W., Verdi, Carla, Wright, Adam D., Milot, Rebecca L., Giustino, Feliciano, Johnston, Michael B. and Herz, Laura M. (2018). Bimolecular recombination in methylammonium lead triiodide perovskite is an inverse absorption process. Nature Communications, 9 (1) 293. doi: 10.1038/s41467-017-02670-2
Crossover from lattice to plasmonic polarons of a spin-polarised electron gas in ferromagnetic EuO
Riley, J. M., Caruso, F., Verdi, C., Duffy, L. B., Watson, M. D., Bawden, L., Volckaert, K., Van Der Laan, G., Hesjedal, T., Hoesch, M., Giustino, F. and King, P. D.C. (2018). Crossover from lattice to plasmonic polarons of a spin-polarised electron gas in ferromagnetic EuO. Nature Communications, 9 (1) 2305, 2305. doi: 10.1038/s41467-018-04749-w
Caruso, Fabio, Verdi, Carla, Poncé, Samuel and Giustino, Feliciano (2018). Electron-plasmon and electron-phonon satellites in the angle-resolved photoelectron spectra of n -doped anatase TiO2. Physical Review B, 97 (16) 165113. doi: 10.1103/PhysRevB.97.165113
Origin of the crossover from polarons to Fermi liquids in transition metal oxides
Verdi, Carla, Caruso, Fabio and Giustino, Feliciano (2017). Origin of the crossover from polarons to Fermi liquids in transition metal oxides. Nature Communications, 8 15769. doi: 10.1038/ncomms15769
Poncï¿½, S., Margine, E. R., Verdi, C. and Giustino, F. (2016). EPW: Electron–phonon coupling, transport and superconducting properties using maximally localized Wannier functions. Computer Physics Communications, 209, 116-133. doi: 10.1016/j.cpc.2016.07.028
Electron-phonon coupling in hybrid lead halide perovskites
Wright, Adam D., Verdi, Carla, Milot, Rebecca L., Eperon, Giles E., Pérez-Osorio, Miguel A., Snaith, Henry J., Giustino, Feliciano, Johnston, Michael B. and Herz, Laura M. (2016). Electron-phonon coupling in hybrid lead halide perovskites. Nature Communications, 7 (1) 11755, 1-9. doi: 10.1038/ncomms11755
Fröhlich electron-phonon vertex from first principles
Verdi, Carla and Giustino, Feliciano (2015). Fröhlich electron-phonon vertex from first principles. Physical Review Letters, 115 (17) 176401. doi: 10.1103/PhysRevLett.115.176401
Filip, Marina R., Verdi, Carla and Giustino, Feliciano (2015). GW Band Structures and Carrier Effective Masses of CH3NH3PbI3 and Hypothetical Perovskites of the Type APbI3: A = NH4, PH4, AsH4, and SbH4. Journal of Physical Chemistry C, 119 (45), 25209-25219. doi: 10.1021/acs.jpcc.5b07891
Verdi, Carla, Mosconi, Edoardo, De Angelis, Filippo, Marsili, Margherita and Umari, P. (2014). Alignment of energy levels in dye/semiconductor interfaces by GW calculations: Effects due to coadsorption of solvent molecules. Physical Review B - Condensed Matter and Materials Physics, 90 (15) 155410. doi: 10.1103/PhysRevB.90.155410
First-principles design of atomic defects for quantum technologies
(2023–2026) ARC Discovery Early Career Researcher Award
First principles calculations of defects in solids for quantum technologies
Doctor Philosophy — Principal Advisor
Other advisors:
New Methods for Strongly Correlated Electrons in Chemically Complex Materials
Doctor Philosophy — Associate Advisor
Other advisors:
Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.
Thermodynamic properties of atomic defects for quantum technologies
Atomic defects in solids are one of the most promising single-photon sources or 'quantum emitters', an important building block for many quantum technologies. In order to design and engineer better quantum emitters, a fundamental understanding of their optical and electronic properties, as well as defect formation and migration, is essential. In this project, first-principles quantum mechanical calculations combined with machine-learning techniques are used in order to uncover key properties such as defect dynamics, formation mechanisms, free energies and stabilities at room and elevated temperatures. The theoretical insights gained in the project aim to inform the design of atomic defects systems for tailored applications as quantum emitters. The student will gain experience with high-performance computing and materials simulation methods, in particular first-principles methods and machine-learned potentials.
Atomistic modelling of solid surfaces and 2D structures
Density functional theory (DFT) is a prominent tool that enables the simulation of materials and molecules at the atomic scale 'from first principles', i.e., without relying on empirical data. To underscore its importance in modern materials physics and beyond, it should suffice to mention that 12 papers on the top-100 list of the most-cited papers of all time, including 2 of the top 10, are all related to DFT. In this project, first-principles DFT calculations will be used to investigate and characterise the structural and electronic properties of 2D structures and solid surfaces. These properties can be directly compared to experimental data, such as scanning tunneling microscopy (STM) experiments conducted in SMP. Target systems include solvated molecules on alkali halide structures, perovskite materials for next-gen solar cells, and oxide structures on metal superconductors.
The student will gain experience with widely used first-principles materials modelling software and high-performance computing.