6 research outputs found
High-performance descriptor for magnetic materials:Accurate discrimination of magnetic symmetries
The magnetic structure is crucial in determining the physical properties
inherent in magnetic compounds. We present an adequate descriptor for magnetic
structure with proper magnetic symmetry and high discrimination performance,
which does not depend on artificial choices for coordinate origin, axis, and
magnetic unit cell in crystal. We extend the formalism called ``smooth overlap
of atomic positions'' (SOAP) providing a numerical representation of atomic
configurations to that of magnetic moment configurations. We introduce the
descriptor in terms of the vector spherical harmonics to describe a magnetic
moment configuration and partial spectra from the expansion coefficients. We
discuss that the lowest order partial spectrum is insufficient to discriminate
the magnetic structures with different magnetic anisotropy, and a higher order
partial spectrum is required in general to characterize detailed magnetic
structures on the same atomic configuration. We then introduce the fourth-order
partial spectrum and evaluate the discrimination performance for different
magnetic structures, mainly focusing on the difference in magnetic symmetry.
The modified partial spectra that are defined not to reflect the difference of
magnetic anisotropy are also useful in evaluating magnetic structures obtained
from first-principles calculations without spin-orbit coupling. We apply the
present method to the symmetry-classified magnetic structures for the crystals
of MnIr and MnSn, which are known to exhibit anomalous transport under
the antiferromagnetic order, and examine the discrimination performance of the
descriptor for different magnetic structures on the same crystal.Comment: 13 pages including supplementary information, 8 figure
Machine learning hydrogen adsorption on nanoclusters through structural descriptors
| openaire: EC/H2020/676580/EU//NoMaDCatalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorption site structures. Machine learning reduces the cost for modelling those sites with the aid of descriptors. We analysed the performance of state-of-the-art structural descriptors Smooth Overlap of Atomic Positions, Many-Body Tensor Representation and Atom-Centered Symmetry Functions while predicting the hydrogen adsorption (free) energy on the surface of nanoclusters. The 2D-material molybdenum disulphide and the alloy copper–gold functioned as test systems. Potential energy scans of hydrogen on the cluster surfaces were conducted to compare the accuracy of the descriptors in kernel ridge regression. By having recourse to data sets of 91 molybdenum disulphide clusters and 24 copper–gold clusters, we found that the mean absolute error could be reduced by machine learning on different clusters simultaneously rather than separately. The adsorption energy was explained by the local descriptor Smooth Overlap of AtomicPositions, combining it with the global descriptor Many-Body Tensor Representation did not improve the overall accuracy. We concluded that fitting of potential energy surfaces could be reduced significantly by merging data from different nanoclusters.Peer reviewe
Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters
Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for computational screening methods and requires the development of new techniques for efficient exploration. We present an automated workflow that systematically manages simulations from the generation of nanoclusters through the submission of production jobs, to the prediction of adsorption energies. The presented workflow was designed to screen nanoclusters of arbitrary shapes and size, but in this work the search was restricted to bimetallic icosahedral clusters and the adsorption was exemplified on the hydrogen evolution reaction. We demonstrate the efficient exploration of nanocluster configurations and screening of adsorption energies with the aid of machine learning. The results show that the maximum of the d-band Hilbert-transform ϵu is correlated strongly with adsorption energies and could be a useful screening property accessible at the nanocluster level.Peer reviewe
DScribe
| openaire: EC/H2020/676580/EU//NoMaD | openaire: EC/H2020/686053/EU//CritCatDScribe is a software package for machine learning that provides popular feature transformations (“descriptors”) for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0. Program summary: Program Title: DScribe Program Files doi: http://dx.doi.org/10.17632/vzrs8n8pk6.1 Licensing provisions: Apache-2.0 Programming language: Python/C/C++ Supplementary material: Supplementary Information as PDF Nature of problem: The application of machine learning for materials science is hindered by the lack of consistent software implementations for feature transformations. These feature transformations, also called descriptors, are a key step in building machine learning models for property prediction in materials science. Solution method: We have developed a library for creating common descriptors used in machine learning applied to materials science. We provide an implementation the following descriptors: Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Functions (ACSF) and Smooth Overlap of Atomic Positions (SOAP). The library has a python interface with computationally intensive routines written in C or C++. The source code, tutorials and documentation are provided online. A continuous integration mechanism is set up to automatically run a series of regression tests and check code coverage when the codebase is updated.Peer reviewe
DScribe: Library of descriptors for machine learning in materials science
DScribe is a software package for machine learning that provides popular feature transformations (“descriptors”) for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0.
Program summary
Program Title: DScribe
Program Files doi: http://dx.doi.org/10.17632/vzrs8n8pk6.1
Licensing provisions: Apache-2.0
Programming language: Python/C/C++
Supplementary material: Supplementary Information as PDF
Nature of problem: The application of machine learning for materials science is hindered by the lack of consistent software implementations for feature transformations. These feature transformations, also called descriptors, are a key step in building machine learning models for property prediction in materials science.
Solution method: We have developed a library for creating common descriptors used in machine learning applied to materials science. We provide an implementation the following descriptors: Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Functions (ACSF) and Smooth Overlap of Atomic Positions (SOAP). The library has a python interface with computationally intensive routines written in C or C++. The source code, tutorials and documentation are provided online. A continuous integration mechanism is set up to automatically run a series of regression tests and check code coverage when the codebase is updated