Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

Medasani, Bharat; Gamst, Anthony; Ding, Hong; Chen, Wei; Persson, Kristin A.; Asta, Mark; Canning, Andrew; Haranczyk, Maciej

doi:10.1038/s41524-016-0001-z

Title: Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

Abstract

We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic (A-B) compounds, using as an example systems with the cubic B2 crystal structure (with equiatomic AB stoichiometry). To the best of our knowledge, this work is the first application of machine learning-models for point defect properties. High throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic compounds. The systems are classified into two groups: (i) those for which the intrinsic defects are antisites for both A and B rich compositions, and (ii) those for which vacancies are the dominant defect for either or both composition ranges. The data was analyzed by machine learning-techniques using decision tree, and full and reduced multiple additive regression tree (MART) models. Among these three schemes, a reduced MART (r-MART) model using six descriptors (formation energy, minimum and difference of electron densities at the Wigner-Seitz cell boundary, atomic radius difference, maximal atomic number and maximal electronegativity) presents the highest fit (98 %) and predictive (75 %) accuracy. This model is used to predict the defect behavior of other B2 compounds, and it is found that 45more »« less

Authors:

Medasani, Bharat ^[1]; Gamst, Anthony ^[2]; Ding, Hong ^[3]; Chen, Wei ^[4]; Persson, Kristin A. ^[3]; Asta, Mark ^[3]; Canning, Andrew ^[5]; Haranczyk, Maciej ^[5]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Univ. of California, San Diego, CA (United States)
Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Illinois Inst. of Technology, Chicago, IL (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Publication Date:: Fri Dec 09 00:00:00 EST 2016

Research Org.:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

OSTI Identifier:: 1494077

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: npj Computational Materials

Additional Journal Information:: Journal Volume: 2; Journal Issue: 1; Journal ID: ISSN 2057-3960

Publisher:: Nature Publishing Group

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Medasani, Bharat, Gamst, Anthony, Ding, Hong, Chen, Wei, Persson, Kristin A., Asta, Mark, Canning, Andrew, and Haranczyk, Maciej. Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning.  United States: N. p., 2016. 
Web.  doi:10.1038/s41524-016-0001-z.

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                    Medasani, Bharat, Gamst, Anthony, Ding, Hong, Chen, Wei, Persson, Kristin A., Asta, Mark, Canning, Andrew, & Haranczyk, Maciej. Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning.  United States.  https://doi.org/10.1038/s41524-016-0001-z

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                    Medasani, Bharat, Gamst, Anthony, Ding, Hong, Chen, Wei, Persson, Kristin A., Asta, Mark, Canning, Andrew, and Haranczyk, Maciej. Fri .  
"Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning".  United States.  https://doi.org/10.1038/s41524-016-0001-z.  https://www.osti.gov/servlets/purl/1494077.

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@article{osti_1494077,

  title        = {Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning},

  author       = {Medasani, Bharat and Gamst, Anthony and Ding, Hong and Chen, Wei and Persson, Kristin A. and Asta, Mark and Canning, Andrew and Haranczyk, Maciej},

  abstractNote = {We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic (A-B) compounds, using as an example systems with the cubic B2 crystal structure (with equiatomic AB stoichiometry). To the best of our knowledge, this work is the first application of machine learning-models for point defect properties. High throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic compounds. The systems are classified into two groups: (i) those for which the intrinsic defects are antisites for both A and B rich compositions, and (ii) those for which vacancies are the dominant defect for either or both composition ranges. The data was analyzed by machine learning-techniques using decision tree, and full and reduced multiple additive regression tree (MART) models. Among these three schemes, a reduced MART (r-MART) model using six descriptors (formation energy, minimum and difference of electron densities at the Wigner-Seitz cell boundary, atomic radius difference, maximal atomic number and maximal electronegativity) presents the highest fit (98 %) and predictive (75 %) accuracy. This model is used to predict the defect behavior of other B2 compounds, and it is found that 45 % of the compounds considered feature vacancies as dominant defects for either A or B rich compositions (or both). The ability to predict dominant defect types is important for the modeling of thermodynamic and kinetic properties of intermetallic compounds, and the present results illustrate how this information can be derived using modern tools combining high throughput calculations and data analytics.},

  doi          = {10.1038/s41524-016-0001-z},

  journal      = {npj Computational Materials},

  number       = 1,

  volume       = 2,

  place        = {United States},

  year         = {Fri Dec 09 00:00:00 EST 2016},

  month        = {Fri Dec 09 00:00:00 EST 2016}

}

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https://doi.org/10.1038/s41524-016-0001-z

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Figures / Tables:

Fig. 1: a Illustration of B2 crystal structure and b the possible dominant defect configurations in B2 intermetallic compounds. BeNi is classified as antisite dominant intermetallic, whereas AlCo and AlRh are considered as non-antisite dominant ones

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Title: Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

Abstract

Citation Formats

Figures / Tables:

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What Are the Best Materials To Separate a Xenon/Krypton Mixture? journal, June 2015

Vacancies in Metals: From First-Principles Calculations to Experimental Data journal, October 2000

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First-principles study of constitutional point defects in B2 NiAl using special quasirandom structures journal, May 2005

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Site preference of transition-metal elements in B2 NiAl: A comprehensive study journal, August 2007

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PyDII: A python framework for computing equilibrium intrinsic point defect concentrations and extrinsic solute site preferences in intermetallic compounds journal, August 2015

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Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints
text, January 2014

On the crystal chemistry of normal valence compounds
journal, December 1959

Classification of octet AB-type binary compounds using dynamical charges: A materials informatics perspective
journal, December 2015

Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
journal, October 1996

Elemental vacancy diffusion database from high-throughput first-principles calculations for fcc and hcp structures
journal, January 2014

The Proof and Measurement of Association between Two Things
journal, October 1987

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
journal, July 2013

Ab-initio based modeling of diffusion in dilute bcc Fe–Ni and Fe–Cr alloys and implications for radiation induced segregation
journal, April 2011

Structure classification and melting temperature prediction in octet AB solids via machine learning
journal, June 2015

A high-throughput infrastructure for density functional theory calculations
journal, June 2011

Site preference of transition-metal elements in B2 NiAl: A comprehensive study
journal, August 2007

Density of constitutional and thermal point defects in ${L 1}_{2}$ ${Al}_{3} Sc$
journal, January 2001

Reducing Dzyaloshinskii-Moriya interaction and field-free spin-orbit torque switching in synthetic antiferromagnets
journal, May 2021

On the occurrence of substitutional and triple defects in intermetallic phases with the B2 structure
journal, August 1980

The Proof and Measurement of Association between Two Things
journal, January 1904

Self diffusion anomaly in ferromagnetic metals: A density-functional-theory investigation of magnetically ordered and disordered Fe and Co
journal, May 2014

On the heat of formation of solid alloys
journal, July 1975

PyDII: A python framework for computing equilibrium intrinsic point defect concentrations and extrinsic solute site preferences in intermetallic compounds
journal, August 2015

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
journal, June 2010

Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
journal, January 2015

The development of Nb-based advanced intermetallic alloys for structural applications
journal, January 1996

The proof and measurement of association between two things
journal, October 2010

Combinatorial screening for new materials in unconstrained composition space with machine learning
journal, March 2014

Light-Weight Intermetallic Titanium Aluminides – Status of Research and Development
journal, July 2011

A general-purpose machine learning framework for predicting properties of inorganic materials
journal, August 2016

Introducing Open boundary conditions in modeling nonperiodic materials and interfaces: the impact of the periodic assumption
preprint, January 2019

Empirical modeling of dopability in diamond-like semiconductors
journal, December 2018

Quantum-Chemical Study of the FeNCN Conversion-Reaction Mechanism in Lithium- and Sodium-Ion Batteries
text, January 2020

Quantum‐Chemical Study of the FeNCN Conversion‐Reaction Mechanism in Lithium‐ and Sodium‐Ion Batteries
journal, January 2020

A Critical Review of Machine Learning of Energy Materials
journal, January 2020

Quantum‐Chemical Study of the FeNCN Conversion‐Reaction Mechanism in Lithium‐ and Sodium‐Ion Batteries
journal, January 2020

Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning
journal, July 2018

Machine learning in materials informatics: recent applications and prospects
journal, December 2017

Machine learning properties of binary wurtzite superlattices
journal, January 2018

Quantum‐Chemical Study of the FeNCN Conversion‐Reaction Mechanism in Lithium‐ and Sodium‐Ion Batteries
text, January 2020

Conditions for void formation in friction stir welding from machine learning
journal, July 2019

A strategy to apply machine learning to small datasets in materials science
journal, May 2018

Instilling defect tolerance in new compounds
journal, September 2017

Understanding and designing magnetoelectric heterostructures guided by computation: progresses, remaining questions, and perspectives
journal, May 2017