Bayesian regression for
multi-level machine-learned potentials
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.
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.
Quantum paraelectricity and structural phase transitions in strontium titanate beyond density functional theoryJournal Article
In: Physical Review Materials, vol. 7, no. 3, pp. l030801, 2023.
Temperature-dependent anharmonic phonons in quantum paraelectric KTaO3 by first principles and machine-learned force fieldsJournal ArticleOpen Access
In: Advanced Quantum Technology, vol. 6, iss. 4, 2023.
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.
Phase transitions of zirconia: Machine-learned force fields beyond density functional theoryJournal Article
In: Physical Review B, vol. 105, no. 6, pp. L060102, 2022.
Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentialsJournal ArticleOpen Access
In: npj Computational Materials, vol. 7, pp. 156, 2021.
α-β phase transition of zirconium predicted by on-the-fly machine-learned force fieldJournal Article
In: Physical Review Materials, vol. 5, no. 5, pp. 053804, 2021.
First-principles hydration free energies of oxygenated species at water–platinum interfacesJournal Article
In: The Journal of Chemical Physics, vol. 154, no. 9, pp. 094107, 2021.
On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic SimulationsJournal Article
In: The Journal of Physical Chemistry Letters, vol. 11, no. 17, pp. 6946–6955, 2020.
Polaron-Driven Surface ReconstructionsJournal ArticleOpen Access
In: Physical Review X, vol. 7, no. 3, pp. 031053, 2017.