Polaron pattern recognition
in correlated oxide surfaces

Subproject P07

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 transition metal oxides.

We address and provide solutions for static and dynamical polaron properties by implementing ML and computer vision approaches to accelerate the exploration of the multi-polaron configurational space and to extend small polaron dynamics to the nanoscale. We integrate charge state encoding into the atomic features and forecast the dynamic evolution by predicting the occupation matrix. These methodological developments will be integrated in our software Leopolad (Learning of polaron dynamics) based on an equivariant graph neural network framework.

Addressing ML-augmented charge (polaron) dynamics is essential for advancing our understanding of complex materials and phenomena, and for fully leveraging ML-assisted MD. By expanding DFT capabilities with ML algorithms, we aim to extend the simulation of polaron dynamics to the nanoscale. This advancement will enable us to uncover novel effects, such as the dynamical interaction of surface polaron with adsorbates.

The advancement of ML-assisted polaron-MD will significantly contribute to the key methodological developments of the SFB, particularly in predicting multivalence states in oxides.

Cesare Franchini
PI

Expertise

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.

Methods:

  • 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

Applications:

  • 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:

  1. Accelerated study of polaron properties by integrating molecular dynamics and machine learning methods (kernel-ridge regression, standard and convolutional neural-networks
  2. Implementation of automated identification of local structures in atomically resolved images using computer vision methods
  3. Complementing the experimental measurements with extensive first principles modeling of perovskite surfaces.

Team

Cesare Franchini
PI

Johanna Paulina Carbone
Co-PI

Daniela Mangano
PhD Student

Associates

Matthias Meier
Postdoc

Luca Leoni
PhD Student

Darin Joseph
PhD Student

Former Members

Michele Reticcioli
Co-PI

Viktor Birschitzky
PhD Student

Marco Corrias
PhD Student

Florian Ellinger
PhD Student

Publications

29 entries « 3 of 3 »

2023

Temperature-dependent anharmonic phonons in quantum paraelectric KTaO3 by first principles and machine-learned force fields

Ranalli, Luigi; Verdi, Carla; Monacelli, Lorenzo; Kresse, Georg; Calandra, Matteo; Franchini, Cesare

Temperature-dependent anharmonic phonons in quantum paraelectric KTaO3 by first principles and machine-learned force fields

Journal ArticleOpen Access

In: Advanced Quantum Technology, vol. 6, iss. 4, 2023.

Abstract | Links | BibTeX | Tags: P03, P07

Automated Real-Space Lattice Extraction for Atomic Force Microscopy Images

Corrias, Marco; Papa, Lorenzo; Sokolovíc, Igor; Birschitzky, Viktor; Gorfer, Alexander; Setvin, Martin; Schmid, Michael; Diebold, Ulrike; Reticcioli, Michele; Franchini, Cesare

Automated Real-Space Lattice Extraction for Atomic Force Microscopy Images

Journal ArticleOpen Access

In: Machine Learning: Science and Technology, vol. 4, pp. 015015, 2023.

Abstract | Links | BibTeX | Tags: P02, P07

2022

Surface chemistry on a polarizable surface: Coupling of CO with KTaO 3(001)

Wang, Zhichang; Reticcioli, Michele; Jakub, Zdenek; Sokolović, Igor; Meier, Matthias; Boatner, Lynn A; Schmid, Michael; Parkinson, Gareth S.; Diebold, Ulrike; Franchini, Cesare; Setvin, Martin

Surface chemistry on a polarizable surface: Coupling of CO with KTaO 3(001)

Journal ArticleOpen Access

In: Science Advances, vol. 8, iss. 33, 2022.

Abstract | Links | BibTeX | Tags: P02, P04, P07

Role of Polarons in Single-Atom Catalysts: Case Study of Me1[Au1,Pt1 and Rh1] on TiO2(110)

Sombut, Panukorn; Puntscher, Lena; Atzmüller, Marlene; Jakub, Zdenek; Reticcioli, Michele; Meier, Matthias; Parkinson, Gareth S.; Franchini, Cesare

Role of Polarons in Single-Atom Catalysts: Case Study of Me1[Au1,Pt1 and Rh1] on TiO2(110)

Journal ArticleOpen Access

In: Topics in Catalysis, vol. 65, pp. 1620–1630, 2022.

Abstract | Links | BibTeX | Tags: P04, P07

Competing electronic states emerging on polar surfaces

Reticcioli, Michele; Wang, Zhichang; Schmid, Michael; Wrana, Dominik; Boatner, Lynn A.; Diebold, Ulrike; Setvin, Martin; Franchini, Cesare

Competing electronic states emerging on polar surfaces

Journal ArticleOpen Access

In: Nature Communications, vol. 13, no. 4311, 2022.

Abstract | Links | BibTeX | Tags: P02, P07

Machine learning for exploring small polaron configurational space

Birschitzky, Viktor C; Ellinger, Florian; Diebold, Ulrike; Reticcioli, Michele; Franchini, Cesare

Machine learning for exploring small polaron configurational space

Journal ArticleOpen Access

In: npj Computational Materials, vol. 8, no. 125, 2022.

Abstract | Links | BibTeX | Tags: P02, P07

CO oxidation by Pt2/Fe3O4: Metastable dimer and support configurations facilitate lattice oxygen extraction

Meier, Matthias; Hulva, Jan; Jakub, Zdenek; Kraushofer, Florian; Bobić, Mislav; Bliem, Roland; Setvin, Martin; Schmid, Michael; Diebold, Ulrike; Franchini, Cesare; Parkinson, Gareth S.

CO oxidation by Pt2/Fe3O4: Metastable dimer and support configurations facilitate lattice oxygen extraction

Journal ArticleOpen Access

In: Science Advances, vol. 8, iss. 13, pp. eabn4580, 2022.

Abstract | Links | BibTeX | Tags: P02, P04, P07

Modeling polarons in density functional theory: lessons learned from TiO2

Reticcioli, Michele; Diebold, Ulrike; Franchini, Cesare

Modeling polarons in density functional theory: lessons learned from TiO2

Journal ArticleOpen Access

In: Journal of Physics: Condensed Matter, vol. 34, no. 20, pp. 204006, 2022.

Abstract | Links | BibTeX | Tags: P02, P07

2021

Rapid oxygen exchange between hematite and water vapor

Jakub, Zdenek; Meier, Matthias; Kraushofer, Florian; Balajka, Jan; Pavelec, Jiri; Schmid, Michael; Franchini, Cesare; Diebold, Ulrike; Parkinson, Gareth S.

Rapid oxygen exchange between hematite and water vapor

Journal ArticleOpen Access

In: Nature Communications, vol. 12, iss. 1, no. 6488, 2021.

Abstract | Links | BibTeX | Tags: P02, P04, P07

29 entries « 3 of 3 »