Machine-learning methods for structure prediction of multi-component perovskites

Subproject P09

The connection between the composition and function of complex multi-component oxides is intricate, and our knowledge about it is extremely limited. Current models can at most predict the stability of a stoichiometric composition, a very general structural feature. P09 will develop accelerated ML models to predict the structural details that determine the functionality of perovskites. We will implement two approaches:

First, EAs will be combined with an NN potential trained on the fly to quickly explore the energy landscape of perovskite surfaces and predict their detailed structures. In collaboration with experimental partners (P02 Diebold, P04 Parkinson), those structures will be falsified by direct comparison with diffraction data on existing surfaces. Additionally, the implementation, inputs, and results of the machine-learned force fields (MLFFs) will be shared with the theoretical partners for cross-validation.

Second, GANs will be trained on known compositions to identify the key features of real perovskite structures and propose new stable ones.

Georg Madsen
PI

Expertise

We develop and apply atomistic models for theoretical chemistry and materials science. Our expertise covers both classical and quantum methods, as well as multiscale calculations and machine-learning techniques. The group has taken part in the development and public release of a range of packages for atomistic calculations, including:

  • WIEN2k, a popular all-electron density functional theory implementation;
  • BoltzTraP and BoltzTraP2, two packages used to interpolate electronic band structures and calculate transport coefficients;
  • ShengBTE, the first open-source solver of the Boltzmann transport for phonons, which enables predictive calculations of the thermal conductivity of nanostructures;
  • almaBTE, a software package for multiscale thermal transport simulation based on first principles;
  • Clinamen, an implementation of the covariance matrix adaptation evolutionary algorithm that helps explore complex energy landscapes.

These are some of the methods we have used to study solids, liquids, surfaces, and nanostructures:

  • Density functional theory (DFT);
  • Classical and ab-initio molecular dynamics (MD);
  • Self-consistent anharmonic free energy calculations;
  • The Boltzmann transport equation (BTE);
  • Traditional and particle-filter Monte Carlo (MC);
  • Covariance matrix adaptation evolutionary algorithm (CMA-ES);
  • Classification and regression random forests based on phenomenological information;
  • Algorithmically differentiable machine-learning (ML) force fields based on JAX;
  • High-throughput (HT) materials screening.

Team

Georg Madsen
PI

Jesús Carrete
co-PI

Florian Buchner
PhD Student

Ralf Wanzenböck
PhD Student

Associates

Péter Kovács
PostDoc

Nico Unglert
PhD Student

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43 entries « 3 of 5 »

2020

Energy-Guided Shape Control Towards Highly Active CeO2

Yang, Jingxia; Ding, Huihui; Wang, Jinjie; Yigit, Nevzat; Xu, Jingli; Rupprechter, Günther; Zhang, Min; Li, Zhiquan

Energy-Guided Shape Control Towards Highly Active CeO2

Journal Article

In: Topics in Catalysis, vol. 63, no. 19-20, pp. 1743–1753, 2020.

Abstract | Links | BibTeX | Tags: P08, pre-TACO

On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations

Jinnouchi, Ryosuke; Miwa, Kazutoshi; Karsai, Ferenc; Kresse, Georg; Asahi, Ryoji

On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations

Journal Article

In: The Journal of Physical Chemistry Letters, vol. 11, no. 17, pp. 6946–6955, 2020.

Abstract | Links | BibTeX | Tags: P03, pre-TACO

Electrochemical Stability of the Reconstructed Fe3O4(001) Surface

Grumelli, Doris; Wiegmann, Tim; Barja, Sara; Reikowski, Finn; Maroun, Fouad; Allongue, Philippe; Balajka, Jan; Parkinson, Gareth S.; Diebold, Ulrike; Kern, Klaus; Magnussen, Olaf M

Electrochemical Stability of the Reconstructed Fe3O4(001) Surface

Journal Article

In: Angewandte Chemie - International Edition, vol. 59, no. 49, pp. 21904–21908, 2020.

Abstract | Links | BibTeX | Tags: P02, P04, pre-TACO

Catalysis by Imaging: From Meso- to Nano-scale

Suchorski, Yuri; Rupprechter, Günther

Catalysis by Imaging: From Meso- to Nano-scale

Journal ArticleOpen Access

In: Topics in Catalysis, vol. 63, no. 15-18, pp. 1532–1544, 2020.

Abstract | Links | BibTeX | Tags: P08, pre-TACO

Probing the Mineral–Water Interface with Nonlinear Optical Spectroscopy

Backus, Ellen H. G.; Schaefer, Jan; Bonn, Mischa

Probing the Mineral–Water Interface with Nonlinear Optical Spectroscopy

Journal ArticleOpen Access

In: Angewandte Chemie - International Edition, vol. 60, no. 19, pp. 10482–10501, 2020.

Abstract | Links | BibTeX | Tags: P11, pre-TACO

The Dynamic Structure of Au38(SR)24 Nanoclusters Supported on CeO2 upon Pretreatment and CO Oxidation

Pollitt, Stephan; Truttmann, Vera; Haunold, Thomas; Garcia, Clara; Olszewski, Wojciech; Llorca, Jordi; é, Noelia Barrab; Rupprechter, Günther

The Dynamic Structure of Au38(SR)24 Nanoclusters Supported on CeO2 upon Pretreatment and CO Oxidation

Journal ArticleOpen Access

In: ACS Catalysis, vol. 10, no. 11, pp. 6144–6148, 2020.

Abstract | Links | BibTeX | Tags: P08, pre-TACO

Surface Charges at the CaF2/Water Interface Allow Very Fast Intermolecular Vibrational-Energy Transfer

Lesnicki, Dominika; Zhang, Zhen; Bonn, Mischa; Sulpizi, Marialore; Backus, Ellen H. G.

Surface Charges at the CaF2/Water Interface Allow Very Fast Intermolecular Vibrational-Energy Transfer

Journal ArticleOpen Access

In: Angewandte Chemie - International Edition, vol. 59, no. 31, pp. 13116–13121, 2020.

Abstract | Links | BibTeX | Tags: P11, pre-TACO

Modifying the Surface Structure of Perovskite-Based Catalysts by Nanoparticle Exsolution

Lindenthal, Lorenz; Rameshan, Raffael; Summerer, Harald; Ruh, Thomas; Popovic, Janko; Nenning, Andreas; Löffler, Stefan; Opitz, Alexander Karl; Blaha, Peter; Rameshan, Christoph

Modifying the Surface Structure of Perovskite-Based Catalysts by Nanoparticle Exsolution

Journal ArticleOpen Access

In: Catalysts, vol. 10, no. 3, pp. 268, 2020.

Abstract | Links | BibTeX | Tags: P10, pre-TACO

2019

Phase stability of the ice XVII-based CO2 chiral hydrate from molecular dynamics simulations

Michl, Jakob; Sega, Marcello; Dellago, Christoph

Phase stability of the ice XVII-based CO2 chiral hydrate from molecular dynamics simulations

Journal ArticleOpen Access

In: The Journal of Chemical Physics, vol. 151, no. 10, pp. 104502, 2019.

Abstract | Links | BibTeX | Tags: P12, pre-TACO

Local Structure and Coordination Define Adsorption in a Model Ir1/Fe3O4 Single-Atom Catalyst

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

Local Structure and Coordination Define Adsorption in a Model Ir1/Fe3O4 Single-Atom Catalyst

Journal ArticleOpen Access

In: Angewandte Chemie - International Edition, vol. 58, no. 39, pp. 13961–13968, 2019.

Abstract | Links | BibTeX | Tags: P02, P04, P07, pre-TACO

43 entries « 3 of 5 »