Development of interatomic potential for Al–Tb alloys using a deep neural network learning method

Tang, L.; Yang, Z. J.; Wen, T. Q.; Ho, K. M.; Kramer, M. J.; Wang, C. Z.

doi:10.1039/d0cp01689f

Title: Development of interatomic potential for Al–Tb alloys using a deep neural network learning method

Journal Article · Wed Jul 22 00:00:00 EDT 2020 · Physical Chemistry Chemical Physics. PCCP

DOI:https://doi.org/10.1039/d0cp01689f· OSTI ID:1660600

^[1]; Yang, Z. J. ^[1]; Wen, T. Q. ^[2]; Ho, K. M. ^[2]; Kramer, M. J. ^[2];

^[2]

Zhejiang Univ. of Technology, Hangzhou (China)
Ames Lab., Ames, IA (United States); Iowa State Univ., Ames, IA (United States)

An interatomic potential for the Al–Tb alloy around the composition of Al₉₀Tb₁₀ is developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total potential energies and forces on each atom obtained from ab initio molecular dynamics (AIMD) simulations are collected to train a DNN model to construct the interatomic potential for the Al–Tb alloy. Here we show that the obtained DNN model can well reproduce the energies and forces calculated by AIMD simulations. Molecular dynamics (MD) simulations using the DNN interatomic potential also accurately describe the structural properties of the Al₉₀Tb₁₀ liquid, such as partial pair correlation functions (PPCFs) and bond angle distributions, in comparison with the results from AIMD simulations. Furthermore, the developed DNN interatomic potential predicts the formation energies of the crystalline phases of the Al–Tb system with an accuracy comparable to ab initio calculations. The structure factors of the Al₉₀Tb₁₀ metallic liquid and glass obtained by MD simulations using the developed DNN interatomic potential are also in good agreement with the experimental X-ray diffraction data. The development of short-range order (SRO) in the Al₉₀Tb₁₀ liquid and the undercooled liquid is also analyzed and three dominant SROs, i.e., Al-centered distorted icosahedron (DISICO) and Tb-centered ‘3661’ and ‘15551’ clusters, respectively, are identified.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Ames Laboratory (AMES), Ames, IA (United States); Iowa State Univ., Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; National Natural Science Foundation of China (NSFC); Natural Science Foundation of Zhejiang Province

Grant/Contract Number:: AC02-07CH11358; 11304279; 11104247; LY18E010007

OSTI ID:: 1660600

Alternate ID(s):: OSTI ID: 1647104

Report Number(s):: IS-J-10,306

Journal Information:: Physical Chemistry Chemical Physics. PCCP, Vol. 22, Issue 33; ISSN 1463-9076

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

References (41)

Accurate force field for molybdenum by machine learning large materials data Chen, Chi; Deng, Zhi; Tran, Richard Physical Review Materials, Vol. 1, Issue 4 https://doi.org/10.1103/PhysRevMaterials.1.043603	journal	September 2017
Generalized Gradient Approximation Made Simple Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868 https://doi.org/10.1103/PhysRevLett.77.3865	journal	October 1996
Unique metallic glass formability and ultra-high tensile strength in AlNiFeGd alloys He, Y.; Dougherty, G. M.; Shiflet, G. J. Acta Metallurgica et Materialia, Vol. 41, Issue 2 https://doi.org/10.1016/0956-7151(93)90064-Y	journal	February 1993
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set Kresse, G.; Furthmüller, J. Computational Materials Science, Vol. 6, Issue 1, p. 15-50 https://doi.org/10.1016/0927-0256(96)00008-0	journal	July 1996
Cooling rate dependence of structural order in Al90Sm10 metallic glass Sun, Yang; Zhang, Yue; Zhang, Feng Journal of Applied Physics, Vol. 120, Issue 1 https://doi.org/10.1063/1.4955223	journal	July 2016
Characterization of a marginal glass former alloy solidified in gas atomized powders Kalay, Y. E.; Chumbley, L. S.; Anderson, I. E. Materials Science and Engineering: A, Vol. 490, Issue 1-2 https://doi.org/10.1016/j.msea.2008.02.032	journal	August 2008
Amorphous, nanoquasicrystalline and nanocrystalline alloys in Al-based systems Inoue, Akihisa Progress in Materials Science, Vol. 43, Issue 5 https://doi.org/10.1016/S0079-6425(98)00005-X	journal	December 1998
Etude de la structure magnetique du compose TbAl par diffraction neutronique Becle, C.; Lemaire, R.; Parthe, E. Solid State Communications, Vol. 6, Issue 2 https://doi.org/10.1016/0038-1098(68)90014-8	journal	February 1968
Neue Perowskitcarbide Haschke, H.; Nowotny, H.; Benesovsky, F. Monatshefte für Chemie - Chemical Monthly, Vol. 98, Issue 2 https://doi.org/10.1007/BF00899941	journal	March 1967
Rare earth-aluminium intermetallic compounds of the form RAl and R3Al2 Buschow, K. H. J. Journal of the Less Common Metals, Vol. 8, Issue 3 https://doi.org/10.1016/0022-5088(65)90047-0	journal	March 1965
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set Kresse, G.; Furthmüller, J. Physical Review B, Vol. 54, Issue 16, p. 11169-11186 https://doi.org/10.1103/PhysRevB.54.11169	journal	October 1996
The crystal structure of rare-earth aluminium compounds R2Al Buschow, K. H. J.; Van Der Goot, A. S. Journal of the Less Common Metals, Vol. 24, Issue 1 https://doi.org/10.1016/0022-5088(71)90175-5	journal	May 1971
Fast Parallel Algorithms for Short-Range Molecular Dynamics Plimpton, Steve Journal of Computational Physics, Vol. 117, Issue 1 https://doi.org/10.1006/jcph.1995.1039	journal	March 1995
Local chemical and topological order in Al–Tb and its role in controlling nanocrystal formation Kalay, Y. E.; Kalay, I.; Hwang, Jinwoo Acta Materialia, Vol. 60, Issue 3 https://doi.org/10.1016/j.actamat.2011.11.008	journal	February 2012
Structural analysis of R2Ni17−xAlx intermetallic compounds Pop, I.; Crişan, V.; Coldea, M. Physica B+C, Vol. 130, Issue 1-3 https://doi.org/10.1016/0378-4363(85)90289-X	journal	May 1985
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Mechanical properties of partially crystallized aluminum based amorphous alloys Kim, Hyoung Seop; Warren, P. J.; Cantor, B. Nanostructured Materials, Vol. 11, Issue 2 https://doi.org/10.1016/S0965-9773(99)00037-9	journal	March 1999
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces Behler, Jörg; Parrinello, Michele Physical Review Letters, Vol. 98, Issue 14 https://doi.org/10.1103/PhysRevLett.98.146401	journal	April 2007
Effect of high pressure on the crystal structures of lanthanide trialuminides Cannon, John F.; Hall, H. Tracy Journal of the Less Common Metals, Vol. 40, Issue 3 https://doi.org/10.1016/0022-5088(75)90076-4	journal	May 1975
Crystal structure of equiatomic ternary compounds: lanthanide-transition metal aluminides Dwight, A. E. Journal of the Less Common Metals, Vol. 102, Issue 1 https://doi.org/10.1016/0022-5088(84)90401-6	journal	September 1984
Neural network interatomic potential for the phase change material GeTe Sosso, Gabriele C.; Miceli, Giacomo; Caravati, Sebastiano Physical Review B, Vol. 85, Issue 17 https://doi.org/10.1103/PhysRevB.85.174103	journal	May 2012
Development of interatomic potentials appropriate for simulation of devitrification of Al ₉₀ Sm ₁₀ alloy Mendelev, M. I.; Zhang, F.; Ye, Z. Modelling and Simulation in Materials Science and Engineering, Vol. 23, Issue 4 https://doi.org/10.1088/0965-0393/23/4/045013	journal	April 2015
Canonical dynamics: Equilibrium phase-space distributions Hoover, William G. Physical Review A, Vol. 31, Issue 3 https://doi.org/10.1103/PhysRevA.31.1695	journal	March 1985
Discovery of a meta-stable Al–Sm phase with unknown stoichiometry using a genetic algorithm Zhang, Feng; McBrearty, Ian; Ott, R. T. Scripta Materialia, Vol. 81 https://doi.org/10.1016/j.scriptamat.2014.02.019	journal	June 2014
Atomistic cluster alignment method for local order mining in liquids and glasses Fang, X. W.; Wang, C. Z.; Yao, Y. X. Physical Review B, Vol. 82, Issue 18 https://doi.org/10.1103/PhysRevB.82.184204	journal	November 2010
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics Wang, Han; Zhang, Linfeng; Han, Jiequn Computer Physics Communications, Vol. 228 https://doi.org/10.1016/j.cpc.2018.03.016	journal	July 2018
Soft self-consistent pseudopotentials in a generalized eigenvalue formalism Vanderbilt, David Physical Review B, Vol. 41, Issue 11, p. 7892-7895 https://doi.org/10.1103/PhysRevB.41.7892	journal	April 1990
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons Bartók, Albert P.; Payne, Mike C.; Kondor, Risi Physical Review Letters, Vol. 104, Issue 13 https://doi.org/10.1103/PhysRevLett.104.136403	journal	April 2010
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials Shapeev, Alexander V. Multiscale Modeling & Simulation, Vol. 14, Issue 3 https://doi.org/10.1137/15M1054183	journal	January 2016
Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds Wen, Tongqi; Wang, Cai-Zhuang; Kramer, M. J. Physical Review B, Vol. 100, Issue 17 https://doi.org/10.1103/PhysRevB.100.174101	journal	November 2019
Discovery of a metastable Al20Sm4 phase Ye, Z.; Zhang, F.; Sun, Y. Applied Physics Letters, Vol. 106, Issue 10 https://doi.org/10.1063/1.4914399	journal	March 2015
Synthesis and Properties of Metallic Glasses That Contain Aluminum He, Y.; Poon, S. J.; Shiflet, G. J. Science, Vol. 241, Issue 4873 https://doi.org/10.1126/science.241.4873.1640	journal	September 1988
Analysis of nanocrystal development in Al-Y-Fe and Al-Sm glasses Foley, J. C.; Allen, D. R.; Perepezko, J. H. Scripta Materialia, Vol. 35, Issue 5 https://doi.org/10.1016/1359-6462(96)00196-0	journal	September 1996
Metallic Glasses Greer, A. L. Science, Vol. 267, Issue 5206 https://doi.org/10.1126/science.267.5206.1947	journal	March 1995
Machine learning of accurate energy-conserving molecular force fields Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E. Science Advances, Vol. 3, Issue 5 https://doi.org/10.1126/sciadv.1603015	journal	May 2017
A unified formulation of the constant temperature molecular dynamics methods Nosé, Shuichi The Journal of Chemical Physics, Vol. 81, Issue 1 https://doi.org/10.1063/1.447334	journal	July 1984
Implementation of the projector augmented-wave method in the ABINIT code: Application to the study of iron under pressure Torrent, Marc; Jollet, François; Bottin, François Computational Materials Science, Vol. 42, Issue 2 https://doi.org/10.1016/j.commatsci.2007.07.020	journal	April 2008
Phase equilibria in aluminium-rich alloys of aluminium-gadolinium and aluminium-terbium Runnalls, O. J. C.; Boucher, R. R. Journal of the Less Common Metals, Vol. 13, Issue 4 https://doi.org/10.1016/0022-5088(67)90036-7	journal	October 1967
The structure of two polymorphic forms of YAl ₃ Bailey, D. M. Acta Crystallographica, Vol. 23, Issue 5 https://doi.org/10.1107/S0365110X67003603	journal	November 1967
New Amorphous Al-Y, Al-La and Al-Ce Alloys Prepared by Melt Spinning Inoue, Akihisa; Ohtera, Katsumasa; Masumoto, Tsuyoshi Japanese Journal of Applied Physics, Vol. 27, Issue Part 2, No. 5 https://doi.org/10.1143/JJAP.27.L736	journal	May 1988
Cooling rate dependence of structural order in Ni ₆₂ Nb ₃₈ metallic glass Wen, Tongqi; Sun, Yang; Ye, Beilin Journal of Applied Physics, Vol. 123, Issue 4 https://doi.org/10.1063/1.5019681	journal	January 2018

Similar Records

Short- and medium-range orders in Al90Tb10 glass and their relation to the structures of competing crystalline phases

Journal Article · Mon Feb 01 00:00:00 EST 2021 · Acta Materialia · OSTI ID:1660600

Tang, L.; Yang, Z. J.; Wen, T. Q.; +3 more

Molecular dynamics simulation of metallic Al–Ce liquids using a neural network machine learning interatomic potential

Journal Article · Tue Nov 16 00:00:00 EST 2021 · Journal of Chemical Physics · OSTI ID:1660600

Tang, L.; Ho, K. M.; Wang, C. Z.

Structural and chemical orders in

N i_{64.5} Z r_{35.5}

metallic glass by molecular dynamics simulation

Journal Article · Tue Mar 06 00:00:00 EST 2018 · Physical Review Materials · OSTI ID:1660600

Tang, L.; Wen, T. Q.; Wang, N.; +5 more

Related Subjects

36 MATERIALS SCIENCE

Title: Development of interatomic potential for Al–Tb alloys using a deep neural network learning method

Citation Formats

References (41)

Similar Records

Related Subjects