4 research outputs found
A local Bayesian optimizer for atomic structures
A local optimization method based on Bayesian Gaussian Processes is developed
and applied to atomic structures. The method is applied to a variety of systems
including molecules, clusters, bulk materials, and molecules at surfaces. The
approach is seen to compare favorably to standard optimization algorithms like
conjugate gradient or BFGS in all cases. The method relies on prediction of
surrogate potential energy surfaces, which are fast to optimize, and which are
gradually improved as the calculation proceeds. The method includes a few
hyperparameters, the optimization of which may lead to further improvements of
the computational speed.Comment: 10 pages, 5 figure
Global optimization of atomic structures with gradient-enhanced Gaussian process regression
Determination of atomic structures is a key challenge in the fields of
computational physics and materials science, as a large variety of mechanical,
chemical, electronic, and optical properties depend sensitively on structure.
Here, we present a global optimization scheme where energy and force
information from density functional theory (DFT) calculations is transferred to
a probabilistic surrogate model to estimate both the potential energy surface
(PES) and the associated uncertainties. The local minima in the surrogate PES
are then used to guide the search for the global minimum in the DFT potential.
We find that adding the gradients in most cases improves the efficiency of the
search significantly. The method is applied to global optimization of
[TaO] clusters with , and the surface structure of
oxidized ZrN
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors
Computational materials screening studies require fast calculation of the
properties of thousands of materials. The calculations are often performed with
Density Functional Theory (DFT), but the necessary computer time sets
limitations for the investigated material space. Therefore, the development of
machine learning models for prediction of DFT calculated properties are
currently of interest. A particular challenge for \emph{new} materials is that
the atomic positions are generally not known. We present a machine learning
model for the prediction of DFT-calculated formation energies based on Voronoi
quotient graphs and local symmetry classification without the need for detailed
information about atomic positions. The model is implemented as a message
passing neural network and tested on the Open Quantum Materials Database (OQMD)
and the Materials Project database. The test mean absolute error is 20 meV on
the OQMD database and 40 meV on Materials Project Database. The possibilities
for prediction in a realistic computational screening setting is investigated
on a dataset of 5976 ABSe selenides with very limited overlap with the OQMD
training set. Pretraining on OQMD and subsequent training on 100 selenides
result in a mean absolute error below 0.1 eV for the formation energy of the
selenides.Comment: 14 pages including references and 13 figure
Machine Learning with bond information for local structure optimizations in surface science
Local optimization of adsorption systems inherently involves different
scales: within the substrate, within the molecule, and between molecule and
substrate. In this work, we show how the explicit modeling of the different
character of the bonds in these systems improves the performance of machine
learning methods for optimization. We introduce an anisotropic kernel in the
Gaussian process regression framework that guides the search for the local
minimum, and we show its overall good performance across different types of
atomic systems. The method shows a speed-up of up to a factor two compared with
the fastest standard optimization methods on adsorption systems. Additionally,
we show that a limited memory approach is not only beneficial in terms of
overall computational resources, but can result in a further reduction of
energy and force calculations