Crystal Structure Prediction of Binary Alloys via Deep Potential

Wang, Haidi; Zhang, Yuzhi; Zhang, Linfeng; Wang, Han

doi:10.3389/fchem.2020.589795

Title: Crystal Structure Prediction of Binary Alloys via Deep Potential

Abstract

Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg₁₂Al₈ shows excellent ductility and Mg₅Al₂₇ has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.

Authors:

Wang, Haidi ^[1]; Zhang, Yuzhi ^[2]; Zhang, Linfeng ^[3]; Wang, Han ^[4]

Hefei Univ. of Technology (China). School of Electronic Science and Applied Physics
Peking Univ., Beijing (China). Yuanpei College; Peking Univ., Beijing (China). Beijing Inst. of Big Data Research
Princeton Univ., NJ (United States). Program in Applied and Computational Mathematics
Inst. of Applied Physics and Computational Mathematics, Beijing (China). Lab. of Computational Physics

Publication Date:: Thu Nov 26 00:00:00 EST 2020

Research Org.:: Princeton Univ., NJ (United States)

Sponsoring Org.:: USDOE Office of Science (SC); National Key R&D Foundation of China; National Natural Science Foundation of China (NSFC)

OSTI Identifier:: 1853193

Grant/Contract Number:: SC0019394; 2016YFB0201200; 2016YFB0201203; 11871110

Resource Type:: Accepted Manuscript

Journal Name:: Frontiers in Chemistry

Additional Journal Information:: Journal Volume: 8; Journal ID: ISSN 2296-2646

Publisher:: Frontiers Research Foundation

Country of Publication:: United States

Language:: English

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Chemistry; many-body potential energy; deep learning; crystal structure prediction; Al-Mg; alloy

Citation Formats


                    Wang, Haidi, Zhang, Yuzhi, Zhang, Linfeng, and Wang, Han. Crystal Structure Prediction of Binary Alloys via Deep Potential.  United States: N. p., 2020. 
Web.  doi:10.3389/fchem.2020.589795.

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                    Wang, Haidi, Zhang, Yuzhi, Zhang, Linfeng, & Wang, Han. Crystal Structure Prediction of Binary Alloys via Deep Potential.  United States.  https://doi.org/10.3389/fchem.2020.589795

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                    Wang, Haidi, Zhang, Yuzhi, Zhang, Linfeng, and Wang, Han. Thu .  
"Crystal Structure Prediction of Binary Alloys via Deep Potential".  United States.  https://doi.org/10.3389/fchem.2020.589795.  https://www.osti.gov/servlets/purl/1853193.

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@article{osti_1853193,

  title        = {Crystal Structure Prediction of Binary Alloys via Deep Potential},

  author       = {Wang, Haidi and Zhang, Yuzhi and Zhang, Linfeng and Wang, Han},

  abstractNote = {Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg12Al8 shows excellent ductility and Mg5Al27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.},

  doi          = {10.3389/fchem.2020.589795},

  journal      = {Frontiers in Chemistry},

  number       = ,

  volume       = 8,

  place        = {United States},

  year         = {Thu Nov 26 00:00:00 EST 2020},

  month        = {Thu Nov 26 00:00:00 EST 2020}

}

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Title: Crystal Structure Prediction of Binary Alloys via Deep Potential

Abstract

Citation Formats

Exploration of crystal structure and the origin of unexpected intrinsic ductility of χCo7Ta2 journal, August 2019

CALYPSO: A method for crystal structure prediction journal, October 2012

XCII. Relations between the elastic moduli and the plastic properties of polycrystalline pure metals journal, August 1954

Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set journal, July 1996

Amp: A modular approach to machine learning in atomistic simulations journal, October 2016

Crystal structure prediction: reflections on present status and challenges journal, January 2018

Superconducting Zirconium Polyhydrides at Moderate Pressures journal, January 2020

The atomic simulation environment—a Python library for working with atoms journal, June 2017

New interatomic potential for Mg–Al–Zn alloys with specific application to dilute Mg-based alloys journal, April 2018

Fast Parallel Algorithms for Short-Range Molecular Dynamics journal, March 1995

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models journal, February 2020

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces journal, April 2007

The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals journal, September 2018

Development of interatomic potentials appropriate for simulation of solid–liquid interface properties in Al–Mg alloys journal, December 2009

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics journal, July 2018

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials journal, January 2016

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 journal, March 2016

Prediction and characterization of an Mg-Al intermetallic compound with potentially improved ductility via orbital-free and Kohn-Sham density functional theory journal, August 2017

Firefly Algorithm for Structural Search journal, June 2016

Modified embedded atom method potential for Al, Si, Mg, Cu, and Fe alloys journal, June 2012

Necessary and sufficient elastic stability conditions in various crystal systems journal, December 2014

USPEX—Evolutionary crystal structure prediction journal, December 2006

Deep Potential: A General Representation of a Many-Body Potential Energy Surface journal, January 2018

Designing chemical analogs to PbTe with intrinsic high band degeneracy and low lattice thermal conductivity journal, February 2019

Predicted Pressure-Induced Superconducting Transition in Electride Li 6 P journal, March 2019

Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning journal, February 2019

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An object-oriented scripting interface to a legacy electronic structure code journal, January 2002

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics journal, April 2018

Active learning of uniformly accurate interatomic potentials for materials simulation journal, February 2019

Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface journal, January 2018

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis journal, February 2013

Designing chemical analogs to PbTe with intrinsic high band degeneracy and low lattice thermal conductivity journal, February 2019

The Computational 2D Materials Database: High-Throughput Modeling and Discovery of Atomically Thin Crystals text, January 2018

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics dataset, January 2018

Exploration of crystal structure and the origin of unexpected intrinsic ductility of χCo7Ta2
journal, August 2019

CALYPSO: A method for crystal structure prediction
journal, October 2012

XCII. Relations between the elastic moduli and the plastic properties of polycrystalline pure metals
journal, August 1954

Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
journal, July 1996

Amp: A modular approach to machine learning in atomistic simulations
journal, October 2016

Crystal structure prediction: reflections on present status and challenges
journal, January 2018

Superconducting Zirconium Polyhydrides at Moderate Pressures
journal, January 2020

The atomic simulation environment—a Python library for working with atoms
journal, June 2017

New interatomic potential for Mg–Al–Zn alloys with specific application to dilute Mg-based alloys
journal, April 2018

Fast Parallel Algorithms for Short-Range Molecular Dynamics
journal, March 1995

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
journal, February 2020

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007

The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals
journal, September 2018

Development of interatomic potentials appropriate for simulation of solid–liquid interface properties in Al–Mg alloys
journal, December 2009

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
journal, July 2018

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
journal, January 2016

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journal, March 2016

Prediction and characterization of an Mg-Al intermetallic compound with potentially improved ductility via orbital-free and Kohn-Sham density functional theory
journal, August 2017

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Modified embedded atom method potential for Al, Si, Mg, Cu, and Fe alloys
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Necessary and sufficient elastic stability conditions in various crystal systems
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USPEX—Evolutionary crystal structure prediction
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Deep Potential: A General Representation of a Many-Body Potential Energy Surface
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Designing chemical analogs to PbTe with intrinsic high band degeneracy and low lattice thermal conductivity
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Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
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Atomic structure of boron resolved using machine learning and global sampling
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Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
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Active learning of uniformly accurate interatomic potentials for materials simulation
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Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface
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Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
journal, February 2013

Designing chemical analogs to PbTe with intrinsic high band degeneracy and low lattice thermal conductivity
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DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
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