Eiaki Morooka - Academia.edu (original) (raw)

Papers by Eiaki Morooka

arXiv (Cornell University), Mar 24, 2023

The Journal of Chemical Physics

We present an update of the DScribe package, a Python library for atomistic descriptors. The upda... more We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe’s descriptor selection with the Valle–Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DScribe. For the many-body tensor representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic derivatives. We demonstrate the effectiveness of the descriptor derivatives for machine learning models of Cu clusters and perovskite alloys.

arXiv (Cornell University), Apr 13, 2023

Nanoclusters add an additional dimension in which to look for promising catalyst candidates, sinc... more 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 Hilberttransform ϵ u is correlated strongly with adsorption energies and could be a useful screening property accessible at the nanocluster level.

Supplementary datasets for "DScribe: Library of Descriptors for Machine Learning in Material... more Supplementary datasets for "DScribe: Library of Descriptors for Machine Learning in Materials Science"

Annals of Physics

The recently discovered APt3P (A=Sr,Ca,La) family of superconductors offers a platform to study f... more The recently discovered APt3P (A=Sr,Ca,La) family of superconductors offers a platform to study frequency dependent superconducting phenomena as the electron-phonon coupling varies from weak to strong. Here we perform ab initio Eliashberg theory calculations to investigate two such phenomena, the occurrence of dip-hump structures in the tunneling spectra and the magnetic field induced coexistence of even and odd frequency superconductivity in these compounds. By calculating the superfluid density, we make predictions for the occurrence of the paramagnetic Meissner effect as a hallmark of odd frequency pairing. Our results provide a link between two seemingly uncorrelated aspects of even and odd frequency superconductivity and provide theoretical guidance for the experimental identification of bulk odd frequency superconductivity in this material's family.

Computer Physics Communications

DScribe is a software package for machine learning that provides popular feature transformations ... more 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.

npj Computational Materials

Catalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorpti... more Catalytic 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 Atomic Positions, 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.