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.
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
Associates
2016
van Roekeghem, Ambroise; Carrete, Jesús; Oses, Corey; Curtarolo, Stefano; Mingo, Natalio
Journal ArticleOpen AccessIn: Physical Review X, vol. 6, no. 4, pp. 041061, 2016.
Abstract | Links | BibTeX | Tags: P09, pre-TACO
@article{Roekeghem2016,
title = {High-Throughput Computation of Thermal Conductivity of High-Temperature Solid Phases: The Case of Oxide and Fluoride Perovskites},
author = {Ambroise van Roekeghem and Jesús Carrete and Corey Oses and Stefano Curtarolo and Natalio Mingo},
doi = {10.1103/physrevx.6.041061},
year = {2016},
date = {2016-06-13},
urldate = {2016-06-13},
journal = {Physical Review X},
volume = {6},
number = {4},
pages = {041061},
publisher = {American Physical Society (APS)},
abstract = {Using finite-temperature phonon calculations and machine-learning methods, we assess the mechanical stability of about 400 semiconducting oxides and fluorides with cubic perovskite structures at 0, 300, and 1000 K. We find 92 mechanically stable compounds at high temperatures—including 36 not mentioned in the literature so far—for which we calculate the thermal conductivity. We show that the thermal conductivity is generally smaller in fluorides than in oxides, largely due to a lower ionic charge, and describe simple structural descriptors that are correlated with its magnitude. Furthermore, we show that the thermal conductivities of most cubic perovskites decrease more slowly than the usual T^{−1} behavior. Within this set, we also screen for materials exhibiting negative thermal expansion. Finally, we describe a strategy to accelerate the discovery of mechanically stable compounds at high temperatures.},
keywords = {P09, pre-TACO},
pubstate = {published},
tppubtype = {article}
}