Bayesian regression for
multi-level machine-learned potentials

Subproject P03

The first-principles description of the properties of multi-component metal oxides is an exceedingly challenging problem. The reasons are that the configurational space grows exponentially with the number of species and standard Density Functional Theory (DFT) is often not accurate enough. The long-term objective of P03 is to accelerate first-principles calculations by developing machine-learning approaches for the description of the interatomic forces, Born effective charges, and other tensorial properties of multivalent oxides. The project will rely on kernel-based methods and Bayesian inference to implement fully automatic “on-the-fly” learning.

In the first project period, we will develop machine-learned force fields (MLFF) for DFT and DFT+U, whereby the number of components in the FF will be gradually increased. A concise framework for learning tensorial properties will be implemented. We will use this to simulate infrared spectra of oxide materials, which can be readily compared to the finite-temperature spectra measured by the experimental groups.

The difference between DFT and hybrid functionals will be machine-learned to go beyond semi-local functionals (Delta-learning). The long-term perspective is to extend this approach to highly accurate beyond-DFT methods, such as the random phase approximation and quantum chemistry (coupled cluster) methods. Although kernel-based methods are exceedingly accurate, they are often less efficient than NN. We will collaborate with other projects to recast the on-the-fly trained FF into NN potentials to address this issue.

Georg Kresse
PI

Expertise

The main research efforts of the group are directed towards the development of quantum-mechanical tools for atomic-scale simulations of properties and processes in materials and the application of these methodologies to key areas of condensed matter physics and materials research. An important pillar of the research is the Vienna Ab initio Simulation Package (VASP), a general-purpose ab initio code for solving the many-electron Schrödinger equation. The code is among the world leaders in its field, with more than 3500 licensees worldwide. We have expertise with simulations for a vast number of properties using many different techniques:

  • Density functional theory (DFT), including spin and non-collinear DFT
  • Linear response theory to calculate phonons and dielectric properties
  • Hartree-Fock techniques and many flavors of hybrid functionals
  • Many-body perturbation theory, including GW and Bethe-Salpeter
  • Wavefunction-based correlated methods (Møller-Plesset perturbation theory)
  • Surface science, including growth and oxide formation
  • Simulation of nanostructures
  • Semiconductor physics: charge trapping, polarons
  • Electronic excitations
  • Defect energies in extended systems

For TACO, we will adapt our machine-learning techniques to tensorial properties and correlated wavefunction techniques. These techniques are directly integrated into VASP and allow to accelerate finite-temperature simulations by many orders of magnitudes.

Team

Georg Kresse
PI

Sylwia Gutowska
co-PI

Payal Wadhwa
PostDoc

Carolin Faller PhD Student, Student Representative 22–24

Bernhard Schmiedmayer
PhD Student

Former Members

Carla Verdi
co-PI

Peitao Liu
PostDoc

Publications

12 entries « 1 of 2 »

2024

Stoichiometric reconstruction of the Al2O3(0001) surface

Hütner, Johanna; Conti, Andrea; Kugler, David; Mittendorfer, Florian; Kresse, Georg; Schmid, Michael; Diebold, Ulrike; Balajka, Jan

Stoichiometric reconstruction of the Al2O3(0001) surface

Journal ArticleOpen AccessSubmittedarXiv

In: 2024.

Abstract | Links | BibTeX | Tags: P02, P03

Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks

de Hijes, Pablo Montero; Dellago, Christoph; Jinnouchi, Ryosuke; Schmiedmayer, Bernhard; Kresse, Georg

Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks

Journal ArticleOpen Access

In: The Journal of Chemical Physics, vol. 160, iss. 11, no. 114107, 2024.

Abstract | Links | BibTeX | Tags: P03, P12

2023

Quantum paraelectricity and structural phase transitions in strontium titanate beyond density functional theory

Verdi, Carla; Ranalli, Luigi; Franchini, Cesare; Kresse, Georg

Quantum paraelectricity and structural phase transitions in strontium titanate beyond density functional theory

Journal Article

In: Physical Review Materials, vol. 7, no. 3, pp. l030801, 2023.

Abstract | Links | BibTeX | Tags: P03, P07

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

Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)

Liu, Peitao; Wang, Jiantao; Avargues, Noah; Verdi, Carla; Singraber, Andreas; Karsai, Ferenc; Chen, Xing-Qiu; Kresse, Georg

Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)

Journal Article

In: Physical Review Letters, vol. 130, no. 7, pp. 078001, 2023.

Abstract | Links | BibTeX | Tags: P03

2022

Hard antiphase domain boundaries in strontium titanate unravelled using machine-learned force fields

Tröster, Andreas; Verdi, Carla; Dellago, Christoph; Rychetsky, Ivan; Kresse, Georg; Schranz, Wilfried

Hard antiphase domain boundaries in strontium titanate unravelled using machine-learned force fields

Journal Article

In: Physical Review Materials, vol. 6, no. 9, pp. 094408, 2022.

Abstract | Links | BibTeX | Tags: P03, P12

Phase transitions of zirconia: Machine-learned force fields beyond density functional theory

Liu, Peitao; Verdi, Carla; Karsai, Ferenc; Kresse, Georg

Phase transitions of zirconia: Machine-learned force fields beyond density functional theory

Journal Article

In: Physical Review B, vol. 105, no. 6, pp. L060102, 2022.

Abstract | Links | BibTeX | Tags: P03

2021

Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials

Verdi, Carla; Karsai, Ferenc; Liu, Peitao; Jinnouchi, Ryosuke; Kresse, Georg

Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials

Journal ArticleOpen Access

In: npj Computational Materials, vol. 7, pp. 156, 2021.

Abstract | Links | BibTeX | Tags: P03

α-β phase transition of zirconium predicted by on-the-fly machine-learned force field

Liu, Peitao; Verdi, Carla; Karsai, Ferenc; Kresse, Georg

α-β phase transition of zirconium predicted by on-the-fly machine-learned force field

Journal Article

In: Physical Review Materials, vol. 5, no. 5, pp. 053804, 2021.

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

First-principles hydration free energies of oxygenated species at water–platinum interfaces

Jinnouchi, Ryosuke; Karsai, Ferenc; Verdi, Carla; Kresse, Georg

First-principles hydration free energies of oxygenated species at water–platinum interfaces

Journal Article

In: The Journal of Chemical Physics, vol. 154, no. 9, pp. 094107, 2021.

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

12 entries « 1 of 2 »