Machine-learning methods for structure prediction of multi-component perovskites
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.
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.
A Differentiable Neural-Network Force Field for Ionic Liquids Journal Article
Pre-Print (arXiv:2106.16220), 2021.
npj Computational Materials, 7 (1), 2021.
Physical Review Materials, 4 (11), pp. 113804, 2020.
Chemistry of Materials, 29 (15), pp. 6220–6227, 2017.
Physical Review X, 6 (4), pp. 041061, 2016.