Polaron pattern recognition
in correlated oxide surfaces
The formation of polarons by charge trapping is pervasive in transition metal oxides. Polarons have been widely studied in binary compounds but comparatively much less so in perovskites.
In P07, we aim to combine advanced first-principles approaches with computer-vision and machine-learning techniques to accelerate and automatize the study of polarons and novel polaron effects in perovskites.
The project has three main pillars: (i) artificial intelligence-aided analysis of experimental ncAFM/STM results (from P02 Diebold, P04 Parkinson) to extract lattice symmetry, surface structure, and chemical composition; (ii) calculation of polaronic configurational energies at different concentrations and temperature using NN; and (iii) identification of unusual types of polarons and polaron-defect complexes in doped perovskites such as spin-, ferroelectric-, Jahn-Teller-, small polarons, and bipolarons.
In the long-term, we plan to establish a fully automatic diagnosis of ncAFM/STM (symmetry, defects, domains) and LEED (diffraction, surface reconstruction) and the construction of a combined experiment & theory database. The research will benefit from two external collaborators and synergy with several experimental (P02, P04) and computational (P03 Kresse, P09 Madsen) TACO partners.
Theoretical and computational modeling of quantum materials, in particular transition metal oxides in bulk phases and surfaces, to predict and interpret novel physical effects and states of matter arising from fundamental quantum interactions: electron-electron correlation, electron-phonon coupling, spin-spin exchange, spin-orbit coupling, to name the most relevant ones. The theoretical research is conducted in strong synergy and cooperation with experimental groups.
- Density functional theory, hybrid functionals, GW, BSE
- First principles molecular dynamics
- Effective Hamiltonian
- Diagrammatic quantum Monte Carlo
- Dynamical mean-field theory
- Machine learning and computer vision
- Polarons: formation, dynamics, polaron-mediated effects, many-body properties
- Computational surface science: energetics, reconstructions, surface polarons, polarity effects, adsorption and chemical reactions
- Quantum magnetism: all-rank multipolar spin-spin interactions beyond Heisenberg exchange
- Electronic and magnetic phase transitions
Our goals in TACO:
- Accelerated study of polaron properties by integrating molecular dynamics and machine learning methods (kernel-ridge regression, standard and convolutional neural-networks
- Implementation of automated identification of local structures in atomically resolved images using computer vision methods
- Complementing the experimental measurements with extensive first principles modeling of perovskite surfaces.
Temperature-dependent anharmonic phonons in quantum paraelectric KTaO3 by first principles and machine-learned force fieldsJournal ArticleForthcomingAccepted Article
In: Advanced Quantum Technology, Forthcoming.
In: Machine Learning: Science and Technology, 2023.
In: Science Advances, vol. 8, iss. 33, 2022.
In: Nature Communications, vol. 13, no. 4311, 2022.
In: Topics in Catalysis, vol. 65, pp. 1620–1630, 2022.
In: npj Computational Materials, vol. 8, no. 125, 2022.
In: ScienceAdvances, vol. 8, iss. 13, pp. eabn4580, 2022.
In: Journal of Physics: Condensed Matter, vol. 34, no. 20, pp. 204006, 2022.
In: Nature Reviews Materials, 2021.
In: Science, vol. 371, no. 6527, pp. 375–379, 2021.