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

Esther Heid
co-PI

Florian Buchner
PhD Student

Ralf Wanzenböck
PhD Student

Associates

Nico Unglert
PhD Student

Former Members

Jesús Carrete
co-PI

Péter Kovács
PostDoc

2024

Neural network enabled molecular dynamics study of HfO2 phase transitions

Bichelmaier, Sebastian; Carrete, Jesús; Madsen, Georg K. H.

Neural network enabled molecular dynamics study of HfO2 phase transitions

Journal ArticleOpen Access

In: Phys. Rev. B, vol. 110, pp. 174105, 2024.

Abstract | Links | BibTeX | Tags: P09

LoGAN: Local generative adversarial network for novel structure prediction

Kovács, Péter; Heid, Esther; Madsen, Georg K. H.

LoGAN: Local generative adversarial network for novel structure prediction

Journal ArticleOpen Access

In: Machine Learning: Science and Technology, vol. 5, no. 3, pp. 035079, 2024.

Abstract | Links | BibTeX | Tags: P09

Digging Its Own Site: Linear Coordination Stabilizes a Pt1/Fe2O3 Single-Atom Catalyst

Rafsanjani-Abbasi, Ali; Buchner, Florian; Lewis, Faith J.; Puntscher, Lena; Kraushofer, Florian; Sombut, Panukorn; Eder, Moritz; Pavelec, Jiří; Rheinfrank, Erik; Franceschi, Giada; Birschitzky, Viktor; Riva, Michele; Franchini, Cesare; Schmid, Michael; Diebold, Ulrike; Meier, Matthias; Madsen, Georg K. H.; Parkinson, Gareth S.

Digging Its Own Site: Linear Coordination Stabilizes a Pt1/Fe2O3 Single-Atom Catalyst

Journal ArticleOpen AccessAccepted Article

In: ACS Nano, 2024.

Abstract | Links | BibTeX | Tags: P02, P04, P07, P09

Exploring Inhomogeneous Surfaces: Ti-rich SrTiO3(110) Reconstructions via Active Learning

Wanzenböck, Ralf; Heid, Esther; Riva, Michele; Franceschi, Giada; Imre, Alexander M.; Carrete, Jesús; Diebold, Ulrike; Madsen, Georg K. H.

Exploring Inhomogeneous Surfaces: Ti-rich SrTiO3(110) Reconstructions via Active Learning

Journal ArticleOpen Access

In: Digital Discovery, vol. 3, pp. 2137-2145, 2024.

Abstract | Links | BibTeX | Tags: P02, P09

Spatially resolved uncertainties for machine learning potentials

Heid, Esther; Schörghuber, Johannes; Wanzenböck, Ralf; Madsen, Georg K. H.

Spatially resolved uncertainties for machine learning potentials

Journal ArticleOpen Access

In: Journal of Chemical Information and Modeling, vol. 64, iss. 16, pp. 6377–6387, 2024.

Abstract | Links | BibTeX | Tags: P09

Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches

Wanzenböck, Ralf; Buchner, Florian; Kovács, Péter; Madsen, Georg K. H.; Carrete, Jesús

Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches

Journal ArticleOpen Access

In: Computer Physics Communications, vol. 297, no. 109065, 2024.

Abstract | Links | BibTeX | Tags: P09

2023

Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning

Carrete, Jesús; Montes-Campos, Hadrián; Wanzenböck, Ralf; Heid, Esther; Madsen, Georg K. H.

Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning

Journal ArticleOpen Access

In: The Journal of Chemical Physics, vol. 158, no. 20, pp. 204801-1–204801-18, 2023.

Abstract | Links | BibTeX | Tags: P09

Neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia

Bichelmaier, Sebastian; Carrete, Jesús; Wanzenböck, Ralf; Buchner, Florian; Madsen, Georg K. H.

Neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia

Journal Article

In: Physical Review B, vol. 107, no. 184111, 2023, ISSN: 2469-9969.

Abstract | Links | BibTeX | Tags: P09

2022

Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions

Wanzenböck, Ralf; Arrigoni, Marco; Bichelmaier, Sebastian; Buchner, Florian; Carrete, Jesús; Madsen, Georg K. H.

Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions

Journal ArticleOpen Access

In: Digital Discovery, vol. 1, no. 5, pp. 703–710, 2022.

Abstract | Links | BibTeX | Tags: P09

A Differentiable Neural-Network Force Field for Ionic Liquids

Montes-Campos, Hadrián; Carrete, Jesús; Bichelmaier, Sebastian; Varela, Luis M; Madsen, Georg K. H.

A Differentiable Neural-Network Force Field for Ionic Liquids

Journal ArticleOpen Access

In: Journal of Chemical Information and Modeling, vol. 62, no. 1, pp. 88–101, 2022.

Abstract | Links | BibTeX | Tags: P09