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Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds

Journal Article · · Physical Review B
 [1];  [2];  [2];  [2];  [3];  [2];  [2];  [4];  [2];  [2];  [5]
  1. Northwestern Polytechnical Univ., Xi’an (China); Ames Lab., Ames, IA (United States); Iowa State Univ., Ames, IA (United States)
  2. Ames Lab., Ames, IA (United States); Iowa State Univ., Ames, IA (United States)
  3. South China Univ. of Technology (SCUT), Guangzhou (China)
  4. Ames Lab., Ames, IA (United States); Iowa State Univ., Ames, IA (United States); Yantai Univ. (China)
  5. Northwestern Polytechnical Univ., Xi’an (China)
+ Show Author Affiliations
Interatomic potentials based on neural-network machine learning (ML) approach to address the long-standing challenge of accuracy versus efficiency in molecular-dynamics simulations have recently attracted a great deal of interest. In this work, utilizing Pd-Si system as a prototype, we extend the development of neural-network ML potentials to compounds exhibiting various types of bonding characteristics. The ML potential is trained by fitting to the energies and forces of both liquid and crystal structures first-principles calculations based on density-functional theory (DFT). We show that the generated ML potential captures the structural features and motifs in Pd82Si18 and Pd75Si25 liquids more accurately than the existing interatomic potential based on embedded-atom method (EAM). The ML potential also describes the solid-liquid interface of these systems very well. Moreover, while the existing EAM potential fails to describe the relative energies of various crystalline structures and predict wrong ground-state structures at Pd3Si and Pd9Si2 composition, the developed ML potential predicts correctly the ground-state structures from genetic algorithm search. The efficient ML potential with DFT accuracy from our study will provide a promising scheme for accurate atomistic simulations of structures and dynamics of complex Pd-Si system.
Research Organization:
Ames Laboratory (AMES), Ames, IA (United States); Iowa State Univ., Ames, IA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Organization:
National Natural Science Foundation of China (NSFC); USDOE Office of Science (SC); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
Grant/Contract Number:
AC02-07CH11358
OSTI ID:
1577810
Journal Information:
Physical Review B, Journal Name: Physical Review B Journal Issue: 17 Vol. 100; ISSN 2469-9950; ISSN PRBMDO
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

References (68)

Fast Parallel Algorithms for Short-Range Molecular Dynamics journal March 1995
Investigation on the photo-induced de-oxygenation process of myoglobin in aqueous solution by use of fluorescence spectroscopy journal April 2008
Relating Dynamic Properties to Atomic Structure in Metallic Glasses journal July 2012
The art of molecular dynamics simulation journal August 1996
Phase diagram calculations for eight glass forming alloy systems journal October 1985
Fitting the Stillinger–Weber potential to amorphous silicon journal April 2001
Eutectic-to-metallic glass transition in the Al–Sm system journal October 2011
Ab initio modeling of metallic Pd80Si20 glass journal December 2012
An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 journal March 2016
Active learning of linearly parametrized interatomic potentials journal December 2017
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials journal January 2019
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics journal July 2018
Recent progress in quantifying glass-forming ability of bulk metallic glasses journal May 2007
Atomic-level structure and structure–property relationship in metallic glasses journal May 2011
The Art of Molecular Dynamics Simulation journal March 2000
The Art of Molecular Dynamics Simulation book January 2004
SchNetPack: A Deep Learning Toolbox For Atomistic Systems journal November 2018
Supercooled liquids and the glass transition journal March 2001
Nucleation mechanism for the direct graphite-to-diamond phase transition journal July 2011
Charge-polarized interfacial superlattices in marginally twisted hexagonal boron nitride journal January 2021
An electrostatic spectral neighbor analysis potential for lithium nitride journal July 2019
Correlation between the Arrhenius crossover and the glass forming ability in metallic glasses journal October 2017
Electronic structure of AlFeN films exhibiting crystallographic orientation change from c- to a-axis with Fe concentrations and annealing effect journal February 2020
‘Crystal Genes’ in Metallic Liquids and Glasses journal March 2016
Si-centered capped trigonal prism ordering in liquid Pd 82 Si 18 alloy study by first-principles calculations journal January 2017
Crystal genes in a marginal glass-forming system of Ni 50 Zr 50 journal January 2017
High‐Temperature Equation of State by a Perturbation Method. I. Nonpolar Gases journal August 1954
Correlation between fragility and eutectic instability and glass-forming ability in binary metallic glasses under growth controlled conditions journal May 2012
Cooling rate dependence of structural order in Al90Sm10 metallic glass journal July 2016
Cooling rate dependence of structural order in Ni 62 Nb 38 metallic glass journal January 2018
SchNet – A deep learning architecture for molecules and materials journal June 2018
Predicting molecular properties with covariant compositional networks journal June 2018
Under what conditions can a glass be formed? journal September 1969
An adaptive genetic algorithm for crystal structure prediction journal December 2013
Development of interatomic potentials appropriate for simulation of devitrification of Al 90 Sm 10 alloy journal April 2015
Effects of Si solute on the glass formation and atomic structure of Pd liquid journal February 2019
Computer "Experiments" on Classical Fluids. I. Thermodynamical Properties of Lennard-Jones Molecules journal July 1967
Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals journal June 1984
Modeling solid-state chemistry: Interatomic potentials for multicomponent systems journal March 1989
Ab initiomolecular dynamics for liquid metals journal January 1993
Projector augmented-wave method journal December 1994
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set journal October 1996
Metallic-covalent interatomic potential for carbon in iron journal October 2008
Atomistic cluster alignment method for local order mining in liquids and glasses journal November 2010
High accuracy and transferability of a neural network potential through charge equilibration for calcium fluoride journal March 2017
Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals journal September 2018
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons journal April 2010
Exploring the Structural Complexity of Intermetallic Compounds by an Adaptive Genetic Algorithm journal January 2014
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics journal April 2018
Unified Approach for Molecular Dynamics and Density-Functional Theory journal November 1985
Generalized Gradient Approximation Made Simple journal October 1996
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces journal April 2007
Accurate force field for molybdenum by machine learning large materials data journal September 2017
Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron journal January 2018
Structural and chemical orders in N i 64.5 Z r 35.5 metallic glass by molecular dynamics simulation journal March 2018
Active learning of uniformly accurate interatomic potentials for materials simulation journal February 2019
Machine Learning a General-Purpose Interatomic Potential for Silicon journal December 2018
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials journal January 2016
Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron text January 2018
Machine Learning a General-Purpose Interatomic Potential for Silicon text January 2018
Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons text January 2009
Accurate Force Field for Molybdenum by Machine Learning Large Materials Data text January 2017
Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron text January 2017
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics text January 2017
SchNet - a deep learning architecture for molecules and materials text January 2017
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials preprint January 2018
Effects of Si solute on the glass formation and atomic structure of Pd liquid text January 2018
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation text January 2018

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75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY
Materials Science
Physics