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.
REFERENCES
1.
L.
Himanen
, M. O. J.
Jäger
, E. V.
Morooka
, F.
Federici Canova
, Y. S.
Ranawat
, D. Z.
Gao
, P.
Rinke
, and A. S.
Foster
, “Dscribe: Library of descriptors for machine learning in materials science
,” Comput. Phys. Commun.
247
, 106949
(2020
).2.
M. F.
Langer
, A.
Goeßmann
, and M.
Rupp
, “Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
,” npj Comput. Mater.
8
, 41
(2022
).3.
M.
Rupp
, A.
Tkatchenko
, K.-R.
Müller
, and O. A.
von Lilienfeld
, “Fast and accurate modeling of molecular atomization energies with machine learning
,” Phys. Rev. Lett.
108
, 058301
(2012
).4.
F.
Faber
, A.
Lindmaa
, O. A.
von Lilienfeld
, and R.
Armiento
, “Crystal structure representations for machine learning models of formation energies
,” Int. J. Quantum Chem.
115
, 1094
–1101
(2015
).5.
H.
Huo
and M.
Rupp
, “Unified representation of molecules and crystals for machine learning
,” Mach. Learn.: Sci. Technol.
3
, 045017
(2022
).6.
J.
Behler
, “Atom-centered symmetry functions for constructing high-dimensional neural network potentials
,” J. Chem. Phys.
134
, 074106
(2011
).7.
A. P.
Bartók
, R.
Kondor
, and G.
Csányi
, “On representing chemical environments
,” Phys. Rev. B
87
, 184115
(2013
).8.
V.
Fung
, J.
Zhang
, E.
Juarez
, and B. G.
Sumpter
, “Benchmarking graph neural networks for materials chemistry
,” npj Comput. Mater.
7
, 84
(2021
).9.
X.-Y.
Zhou
, J.-H.
Zhu
, Y.
Wu
, X.-S.
Yang
, T.
Lookman
, and H.-H.
Wu
, “Machine learning assisted design of FeCoNiCrMn high-entropy alloys with ultra-low hydrogen diffusion coefficients
,” Acta Mater.
224
, 117535
(2022
).10.
A.
Pihlajamäki
, J.
Hämäläinen
, J.
Linja
, P.
Nieminen
, S.
Malola
, T.
Kärkkäinen
, and H.
Häkkinen
, “Monte Carlo simulations of Au38(SCH3)24 nanocluster using distance-based machine learning methods
,” J. Phys. Chem. A
124
, 4827
–4836
(2020
).11.
O.
Rahaman
and A.
Gagliardi
, “Deep learning total energies and orbital energies of large organic molecules using hybridization of molecular fingerprints
,” J. Chem. Inf. Model.
60
, 5971
–5983
(2020
).12.
Q.
Sun
, Y.
Xiang
, Y.
Liu
, L.
Xu
, T.
Leng
, Y.
Ye
, A.
Fortunelli
, W. A.
Goddard
, and T.
Cheng
, “Machine learning predicts the x-ray photoelectron spectroscopy of the solid electrolyte interface of lithium metal battery
,” J. Phys. Chem. Lett.
13
, 8047
–8054
(2022
).13.
H.
Hirai
, T.
Iizawa
, T.
Tamura
, M.
Karasuyama
, R.
Kobayashi
, and T.
Hirose
, “Machine-learning-based prediction of first-principles XANES spectra for amorphous materials
,” Phys. Rev. Mater.
6
, 115601
(2022
).14.
M. P.
Lourenço
, L. B.
Herrera
, J.
Hostaš
, P.
Calaminici
, A. M.
Köster
, A.
Tchagang
, and D. R.
Salahub
, “Taking the multiplicity inside the loop: Active learning for structural and spin multiplicity elucidation of atomic clusters
,” Theor. Chem. Acc.
140
, 116
(2021
).15.
L.
Sun
, Y.-X.
Zhou
, X.-D.
Wang
, Y.-H.
Chen
, V. L.
Deringer
, R.
Mazzarello
, and W.
Zhang
, “Ab initio molecular dynamics and materials design for embedded phase-change memory
,” npj Comput. Mater.
7
, 29
(2021
).16.
B.
Cheng
, R.-R.
Griffiths
, S.
Wengert
, C.
Kunkel
, T.
Stenczel
, B.
Zhu
, V. L.
Deringer
, N.
Bernstein
, J. T.
Margraf
, K.
Reuter
, and G.
Csanyi
, “Mapping materials and molecules
,” Acc. Chem. Res.
53
, 1981
–1991
(2020
).17.
B.
Monserrat
, J. G.
Brandenburg
, E. A.
Engel
, and B.
Cheng
, “Liquid water contains the building blocks of diverse ice phases
,” Nat. Commun.
11
, 5757
(2020
).18.
M.
Valle
and A. R.
Oganov
, “Crystal fingerprint space—a novel paradigm for studying crystal-structure sets
,” Acta Crystallogr. A
66
, 507
–517
(2010
).19.
M. K.
Bisbo
and B.
Hammer
, “Efficient global structure optimization with a machine-learned surrogate model
,” Phys. Rev. Lett.
124
, 086102
(2020
).20.
M.
Arrigoni
and G. K. H.
Madsen
, “Evolutionary computing and machine learning for discovering of low-energy defect configurations
,” npj Comput. Mater.
7
, 71
(2021
).21.
M. K.
Bisbo
and B.
Hammer
, “Global optimization of atomic structure enhanced by machine learning
,” Phys. Rev. B
105
, 245404
(2022
).22.
A.
Stuke
, M.
Todorović
, M.
Rupp
, C.
Kunkel
, K.
Ghosh
, L.
Himanen
, and P.
Rinke
, “Chemical diversity in molecular orbital energy predictions with kernel ridge regression
,” J. Chem. Phys.
150
, 204121
(2019
).23.
Y.
Jiang
, D.
Chen
, X.
Chen
, T.
Li
, G.-W.
Wei
, and F.
Pan
, “Topological representations of crystalline compounds for the machine-learning prediction of materials properties
,” npj Comput. Mater.
7
, 28
(2021
).24.
J.
Behler
and M.
Parrinello
, “Generalized neural-network representation of high-dimensional potential-energy surfaces
,” Phys. Rev. Lett.
98
, 146401
(2007
).25.
A. P.
Bartók
, M. C.
Payne
, R.
Kondor
, and G.
Csányi
, “Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
,” Phys. Rev. Lett.
104
, 136403
(2010
).26.
K. T.
Schütt
, H. E.
Sauceda
, P.-J.
Kindermans
, A.
Tkatchenko
, and K.-R.
Müller
, “SchNet—a deep learning architecture for molecules and materials
,” J. Chem. Phys.
148
, 241722
(2018
).27.
G.
Csányi
, S.
Winfield
, J. R.
Kermode
, A.
De Vita
, A.
Comisso
, N.
Bernstein
, and M. C.
Payne
, “Expressive programming for computational physics in Fortran 95+
,” IoP Computational Physics Newsletter, Spring 2007 (2007
).28.
J.
Laakso
, M.
Todorović
, J.
Li
, G.-X.
Zhang
, and P.
Rinke
, “Compositional engineering of perovskites with machine learning
,” Phys. Rev. Mater.
6
, 113801
(2022
).29.
S.
De
, A. P.
Bartók
, G.
Csányi
, and M.
Ceriotti
, “Comparing molecules and solids across structural and alchemical space
,” Phys. Chem. Chem. Phys.
18
, 13754
–13769
(2016
).30.
S. M.
Foiles
, M. I.
Baskes
, and M. S.
Daw
, “Embedded-atom-method functions for the fcc metals Cu, Ag, Au, Ni, Pd, Pt, and their alloys
,” Phys. Rev. B
33
, 7983
–7991
(1986
).31.
W. G.
Hoover
, “Canonical dynamics: Equilibrium phase-space distributions
,” Phys. Rev. A
31
, 1695
–1697
(1985
).32.
A. P.
Thompson
, H. M.
Aktulga
, R.
Berger
, D. S.
Bolintineanu
, W. M.
Brown
, P. S.
Crozier
, P.
J’t Veld
, A.
Kohlmeyer
, S. G.
Moore
, T. D.
Nguyen
, R.
Shan
, M. J.
Stevens
, J.
Tranchida
, C.
Trott
, and S. J.
Plimpton
, “LAMMPS—A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
,” Comput. Phys. Commun.
271
, 108171
(2022
).33.
34.
M.
Abadi
, A.
Agarwal
, P.
Barham
, E.
Brevdo
, Z.
Chen
, C.
Citro
, G. S.
Corrado
, A.
Davis
, J.
Dean
, M.
Devin
, S.
Ghemawat
, I.
Goodfellow
, A.
Harp
, G.
Irving
, M.
Isard
, Y.
Jia
, R.
Jozefowicz
, L.
Kaiser
, M.
Kudlur
, J.
Levenberg
, D.
Mané
, R.
Monga
, S.
Moore
, D.
Murray
, C.
Olah
, M.
Schuster
, J.
Shlens
, B.
Steiner
, I.
Sutskever
, K.
Talwar
, P.
Tucker
, V.
Vanhoucke
, V.
Vasudevan
, F.
Viégas
, O.
Vinyals
, P.
Warden
, M.
Wattenberg
, M.
Wicke
, Y.
Yu
, and X.
Zheng
, “TensorFlow: Large-scale machine learning on heterogeneous systems
,” arXiv:1603.04467 (2015
).35.
36.
A. H.
Larsen
, J. J.
Mortensen
, J.
Blomqvist
, I. E.
Castelli
, R.
Christensen
, M.
Dułak
, J.
Friis
, M. N.
Groves
, B.
Hammer
, C.
Hargus
, E. D.
Hermes
, P. C.
Jennings
, P. B.
Jensen
, J.
Kermode
, J. R.
Kitchin
, E. L.
Kolsbjerg
, J.
Kubal
, K.
Kaasbjerg
, S.
Lysgaard
, J. B.
Maronsson
, T.
Maxson
, T.
Olsen
, L.
Pastewka
, A.
Peterson
, C.
Rostgaard
, J.
Schiøtz
, O.
Schütt
, M.
Strange
, K. S.
Thygesen
, T.
Vegge
, L.
Vilhelmsen
, M.
Walter
, Z.
Zeng
, and K. W.
Jacobsen
, “The atomic simulation environment—A Python library for working with atoms
,” J. Phys.: Condens. Matter
29
, 273002
(2017
).37.
C. R.
Harris
, K. J.
Millman
, S. J.
van der Walt
, R.
Gommers
, P.
Virtanen
, D.
Cournapeau
, E.
Wieser
, J.
Taylor
, S.
Berg
, N. J.
Smith
, R.
Kern
, M.
Picus
, S.
Hoyer
, M. H.
van Kerkwijk
, M.
Brett
, A.
Haldane
, J. F.
del Río
, M.
Wiebe
, P.
Peterson
, P.
Gérard-Marchant
, K.
Sheppard
, T.
Reddy
, W.
Weckesser
, H.
Abbasi
, C.
Gohlke
, and T. E.
Oliphant
, “Array programming with NumPy
,” Nature
585
, 357
–362
(2020
).38.
P.
Virtanen
, R.
Gommers
, T. E.
Oliphant
, M.
Haberland
, T.
Reddy
, D.
Cournapeau
, E.
Burovski
, P.
Peterson
, W.
Weckesser
, J.
Bright
, S. J.
van der Walt
, M.
Brett
, J.
Wilson
, K. J.
Millman
, N.
Mayorov
, A. R. J.
Nelson
, E.
Jones
, R.
Kern
, E.
Larson
, C. J.
Carey
, İ.
Polat
, Y.
Feng
, E. W.
Moore
, J.
VanderPlas
, D.
Laxalde
, J.
Perktold
, R.
Cimrman
, I.
Henriksen
, E. A.
Quintero
, C. R.
Harris
, A. M.
Archibald
, A. H.
Ribeiro
, F.
Pedregosa
, P.
van Mulbregt
, and SciPy 1.0 Contributors
, “SciPy 1.0: Fundamental algorithms for scientific computing in Python
,” Nat. Methods
17
, 261
–272
(2020
).39.
W.
Jakob
, J.
Rhinelander
, and D.
Moldovan
, “pybind11 – seamless operability between C++11 and Python
,” https://github.com/pybind/pybind11, 2017
.40.
See https://pypi.org/ for Python Package Index—PyPI.
41.
Conda-Forge Community
(2015
). “The Conda-Forge Project: Community-based software distribution built on the Conda Package format and ecosystem
,” Zenodo. https://doi.org/10.5281/zenodo.4774217© 2023 Author(s). Published under an exclusive license by AIP Publishing.
2023
Author(s)
You do not currently have access to this content.
