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Title: Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

Abstract

With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method–dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, but there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method–sensitive predictor for metallic glasses that improves performance with usemore » and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path–dependent and that current physiochemical theories find challenging to predict.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo;  [4];  [5];  [1]
  1. SLAC National Accelerator Lab., Menlo Park, CA (United States). Stanford Synchrotron Radiation Lightsource (SSRL)
  2. Northwestern Univ., Evanston, IL (United States). Dept. of Materials Science and Engineering; Univ. of Chicago, IL (United States). Computational Institute
  3. Univ. of South Carolina, Columbia, SC (United States). College of Engineering and Computing
  4. Univ. of New South Wales, Sydney, NSW (Australia). School of Materials Science and Engineering
  5. National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States). Materials for Energy and Sustainable Development Group
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22) Funder #1: National Science Foundation Funder ID: (FUNDREF) 10.13039/100000001 Grant: 1250052 Funder #2: U.S. Department of Energy Funder ID: (FUNDREF) 10.13039/100000015 Grant: DE-AC02-76SF00515 Funder #3: U.S. Department of Energy Funder ID: (FUNDREF) 10.13039/100000015 Grant: FWP-100250 Funder #4: U.S. Department of Commerce Funder ID: (FUNDREF) 10.13039/100000190 Grant: 70NANB14H012
OSTI Identifier:
1421387
Grant/Contract Number:  
AC02-76SF00515
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Science Advances
Additional Journal Information:
Journal Volume: 4; Journal Issue: 4; Journal ID: ISSN 2375-2548
Publisher:
AAAS
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Ren, Fang, Ward, Logan, Williams, Travis, Laws, Kevin J., Wolverton, Christopher, Hattrick-Simpers, Jason, and Mehta, Apurva. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. United States: N. p., 2018. Web. doi:10.1126/sciadv.aaq1566.
Ren, Fang, Ward, Logan, Williams, Travis, Laws, Kevin J., Wolverton, Christopher, Hattrick-Simpers, Jason, & Mehta, Apurva. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. United States. doi:10.1126/sciadv.aaq1566.
Ren, Fang, Ward, Logan, Williams, Travis, Laws, Kevin J., Wolverton, Christopher, Hattrick-Simpers, Jason, and Mehta, Apurva. Sun . "Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments". United States. doi:10.1126/sciadv.aaq1566. https://www.osti.gov/servlets/purl/1421387.
@article{osti_1421387,
title = {Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments},
author = {Ren, Fang and Ward, Logan and Williams, Travis and Laws, Kevin J. and Wolverton, Christopher and Hattrick-Simpers, Jason and Mehta, Apurva},
abstractNote = {With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method–dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, but there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method–sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path–dependent and that current physiochemical theories find challenging to predict.},
doi = {10.1126/sciadv.aaq1566},
journal = {Science Advances},
issn = {2375-2548},
number = 4,
volume = 4,
place = {United States},
year = {2018},
month = {4}
}

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Works referenced in this record:

A predictive structural model for bulk metallic glasses
journal, September 2015

  • Laws, K. J.; Miracle, D. B.; Ferry, M.
  • Nature Communications, Vol. 6, Issue 1
  • DOI: 10.1038/ncomms9123

Zero‐magnetostriction and low field magnetic properties of Co‐TM‐Zr amorphous alloys (TM = V, Cr, Mo or W)
journal, March 1981

  • Nose, M.; Kanehira, J.; Ohnuma, S.
  • Journal of Applied Physics, Vol. 52, Issue 3
  • DOI: 10.1063/1.329567

Thermodynamic prediction of bulk metallic glass forming alloys in ternary Zr–Cu–X (X=Ag, Al, Ti, Ga) systems
journal, October 2011


Combinatorial development of bulk metallic glasses
journal, April 2014

  • Ding, Shiyan; Liu, Yanhui; Li, Yanglin
  • Nature Materials, Vol. 13, Issue 5
  • DOI: 10.1038/nmat3939

Data-Driven Review of Thermoelectric Materials: Performance and Resource Considerations
journal, May 2013

  • Gaultois, Michael W.; Sparks, Taylor D.; Borg, Christopher K. H.
  • Chemistry of Materials, Vol. 25, Issue 15
  • DOI: 10.1021/cm400893e

A Semi-Empirical Model for Tilted-Gun Planar Magnetron Sputtering Accounting for Chimney Shadowing
journal, December 2014


Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases
journal, August 2016

  • Perim, Eric; Lee, Dongwoo; Liu, Yanhui
  • Nature Communications, Vol. 7, Issue 1
  • DOI: 10.1038/ncomms12315

High-throughput discovery and characterization of multicomponent bulk metallic glass alloys
journal, November 2016


On the origin of multi-component bulk metallic glasses: Atomic size mismatches and de-mixing
journal, August 2015

  • Zhang, Kai; Dice, Bradley; Liu, Yanhui
  • The Journal of Chemical Physics, Vol. 143, Issue 5
  • DOI: 10.1063/1.4927560

A combinatorial thin film sputtering approach for synthesizing and characterizing ternary ZrCuAl metallic glasses
journal, September 2007


Perspective: Codesign for materials science: An optimal learning approach
journal, April 2016

  • Lookman, Turab; Alexander, Francis J.; Bishop, Alan R.
  • APL Materials, Vol. 4, Issue 5
  • DOI: 10.1063/1.4944627

Bulk metallic glasses
journal, June 2004

  • Wang, W. H.; Dong, C.; Shek, C. H.
  • Materials Science and Engineering: R: Reports, Vol. 44, Issue 2-3
  • DOI: 10.1016/j.mser.2004.03.001

Super Plastic Bulk Metallic Glasses at Room Temperature
journal, March 2007


Thermal stability and electronic properties of amorphous Zr-Co and Zr-Ni alloys
journal, April 1979


Technique for high axial shielding factor performance of large-scale, thin, open-ended, cylindrical Metglas magnetic shields
journal, July 2011

  • Malkowski, S.; Adhikari, R.; Hona, B.
  • Review of Scientific Instruments, Vol. 82, Issue 7
  • DOI: 10.1063/1.3605665

Organic Glasses with Exceptional Thermodynamic and Kinetic Stability
journal, January 2007


A damage-tolerant glass
journal, January 2011

  • Demetriou, Marios D.; Launey, Maximilien E.; Garrett, Glenn
  • Nature Materials, Vol. 10, Issue 2
  • DOI: 10.1038/nmat2930

Magnetic properties, phase evolution, and microstructure of the Co–Zr–V ribbons
journal, November 2013


Ultrastable glasses from in silico vapour deposition
journal, January 2013

  • Singh, Sadanand; Ediger, M. D.; de Pablo, Juan J.
  • Nature Materials, Vol. 12, Issue 2
  • DOI: 10.1038/nmat3521

Corrosion behaviour of metallic glasses
journal, September 1981

  • Waseda, Y.; Aust, K. T.
  • Journal of Materials Science, Vol. 16, Issue 9
  • DOI: 10.1007/BF01113569

Sputtering of amorphous Co‐Zr and Co‐Hf films with soft magnetic properties
journal, April 1982

  • Shimada, Yutaka; Kojima, Hiroshi
  • Journal of Applied Physics, Vol. 53, Issue 4
  • DOI: 10.1063/1.331013

Precipitation of amorphous SiO2 particles and their properties
journal, March 2011


Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials
journal, June 2013

  • Green, Martin L.; Takeuchi, Ichiro; Hattrick-Simpers, Jason R.
  • Journal of Applied Physics, Vol. 113, Issue 23
  • DOI: 10.1063/1.4803530

Prediction of high-entropy stabilized solid-solution in multi-component alloys
journal, February 2012


On-the-Fly Data Assessment for High-Throughput X-ray Diffraction Measurements
journal, May 2017


Machine-learning-assisted materials discovery using failed experiments
journal, May 2016

  • Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.
  • Nature, Vol. 533, Issue 7601
  • DOI: 10.1038/nature17439

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


High Strength Bulk Amorphous Alloys with Low Critical Cooling Rates (<I>Overview</I>)
journal, January 1995


Under what conditions can a glass be formed?
journal, September 1969


Random Forests
journal, January 2001


The WEKA data mining software: an update
journal, November 2009

  • Hall, Mark; Frank, Eibe; Holmes, Geoffrey
  • ACM SIGKDD Explorations Newsletter, Vol. 11, Issue 1
  • DOI: 10.1145/1656274.1656278

The random subspace method for constructing decision forests
journal, January 1998

  • Tin Kam Ho,
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, Issue 8
  • DOI: 10.1109/34.709601

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


Receiver Operating Characteristic Curves and Their Use in Radiology
journal, October 2003


Substrate’s surface electrons disrupt molecular self-assembly
journal, November 2017


    Works referencing / citing this record:

    Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses
    journal, April 2019

    • Xiong, Jie; Zhang, Tong-Yi; Shi, San-Qiang
    • MRS Communications, Vol. 9, Issue 02
    • DOI: 10.1557/mrc.2019.44

    Machine learning of optical properties of materials – predicting spectra from images and images from spectra
    journal, January 2019

    • Stein, Helge S.; Guevarra, Dan; Newhouse, Paul F.
    • Chemical Science, Vol. 10, Issue 1
    • DOI: 10.1039/c8sc03077d

    Enumeration of de novo inorganic complexes for chemical discovery and machine learning
    journal, January 2020

    • Gugler, Stefan; Janet, Jon Paul; Kulik, Heather J.
    • Molecular Systems Design & Engineering, Vol. 5, Issue 1
    • DOI: 10.1039/c9me00069k

    A quantitative uncertainty metric controls error in neural network-driven chemical discovery
    journal, January 2019

    • Janet, Jon Paul; Duan, Chenru; Yang, Tzuhsiung
    • Chemical Science, Vol. 10, Issue 34
    • DOI: 10.1039/c9sc02298h

    Machine learning of optical properties of materials – predicting spectra from images and images from spectra
    journal, January 2019

    • Stein, Helge S.; Guevarra, Dan; Newhouse, Paul F.
    • Chemical Science, Vol. 10, Issue 1
    • DOI: 10.1039/c8sc03077d

    Enumeration of de novo inorganic complexes for chemical discovery and machine learning
    journal, January 2020

    • Gugler, Stefan; Janet, Jon Paul; Kulik, Heather J.
    • Molecular Systems Design & Engineering, Vol. 5, Issue 1
    • DOI: 10.1039/c9me00069k

    A quantitative uncertainty metric controls error in neural network-driven chemical discovery
    journal, January 2019

    • Janet, Jon Paul; Duan, Chenru; Yang, Tzuhsiung
    • Chemical Science, Vol. 10, Issue 34
    • DOI: 10.1039/c9sc02298h

    Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses
    journal, April 2019

    • Xiong, Jie; Zhang, Tong-Yi; Shi, San-Qiang
    • MRS Communications, Vol. 9, Issue 02
    • DOI: 10.1557/mrc.2019.44

    Recent Advances in Metallic Glass Nanostructures: Synthesis Strategies and Electrocatalytic Applications
    journal, December 2018

    • Li, Jinyang; Doubek, Gustavo; McMillon-Brown, Lyndsey
    • Advanced Materials, Vol. 31, Issue 7
    • DOI: 10.1002/adma.201802120

    Data‐Driven Materials Science: Status, Challenges, and Perspectives
    journal, September 2019

    • Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
    • Advanced Science, Vol. 6, Issue 21
    • DOI: 10.1002/advs.201900808

    Functional Applications of Metallic Glasses in Electrocatalysis
    journal, April 2019


    Synchrotron Big Data Science
    journal, September 2018


    Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning
    journal, May 2019


    Computational Platform for Manufacturing Bulk Metallic Glasses Based on GFA Parameters
    journal, September 2018

    • Kuthe, Sudhanshu; Deshmukh, Akash; Palikundwar, Umesh
    • Transactions of the Indian Institute of Metals, Vol. 71, Issue 11
    • DOI: 10.1007/s12666-018-1416-7

    Structural Characteristic Length in Metallic Glasses
    journal, January 2020


    Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry
    journal, October 2018

    • Bartel, Christopher J.; Millican, Samantha L.; Deml, Ann M.
    • Nature Communications, Vol. 9, Issue 1
    • DOI: 10.1038/s41467-018-06682-4

    Unsupervised discovery of solid-state lithium ion conductors
    journal, November 2019


    A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns
    journal, January 2020


    Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
    journal, February 2019

    • Lookman, Turab; Balachandran, Prasanna V.; Xue, Dezhen
    • npj Computational Materials, Vol. 5, Issue 1
    • DOI: 10.1038/s41524-019-0153-8

    Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures
    journal, March 2019


    Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
    journal, May 2019


    Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
    journal, January 2020


    Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams
    journal, January 2019


    Accelerating small-angle scattering experiments on anisotropic samples using kernel density estimation
    journal, February 2019


    A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering
    journal, August 2019


    Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
    journal, January 2018

    • Meredig, Bryce; Antono, Erin; Church, Carena
    • Molecular Systems Design & Engineering, Vol. 3, Issue 5
    • DOI: 10.1039/c8me00012c

    Nanoinformatics, and the big challenges for the science of small things
    journal, January 2019

    • Barnard, A. S.; Motevalli, B.; Parker, A. J.
    • Nanoscale, Vol. 11, Issue 41
    • DOI: 10.1039/c9nr05912a

    A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
    journal, April 2019

    • Cheng, Lixue; Welborn, Matthew; Christensen, Anders S.
    • The Journal of Chemical Physics, Vol. 150, Issue 13
    • DOI: 10.1063/1.5088393

    Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data
    journal, June 2019

    • Cubuk, Ekin D.; Sendek, Austin D.; Reed, Evan J.
    • The Journal of Chemical Physics, Vol. 150, Issue 21
    • DOI: 10.1063/1.5093220

    Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO 3 thin films
    journal, October 2019

    • Wakabayashi, Yuki K.; Otsuka, Takuma; Krockenberger, Yoshiharu
    • APL Materials, Vol. 7, Issue 10
    • DOI: 10.1063/1.5123019

    Model-driven design of bioactive glasses: from molecular dynamics through machine learning
    journal, December 2019


    Prediction and optimization of epoxy adhesive strength from a small dataset through active learning
    journal, October 2019

    • Pruksawan, Sirawit; Lambard, Guillaume; Samitsu, Sadaki
    • Science and Technology of Advanced Materials, Vol. 20, Issue 1
    • DOI: 10.1080/14686996.2019.1673670

    Machine Learning to Instruct Single Crystal Growth by Flux Method
    journal, May 2019


    From DFT to machine learning: recent approaches to materials science–a review
    journal, May 2019

    • Schleder, Gabriel R.; Padilha, Antonio C. M.; Acosta, Carlos Mera
    • Journal of Physics: Materials, Vol. 2, Issue 3
    • DOI: 10.1088/2515-7639/ab084b

    Formation criterion for binary metal diboride solid solutions established through combinatorial methods
    journal, January 2020

    • Wen, Tongqi; Ye, Beilin; Liu, Honghua
    • Journal of the American Ceramic Society, Vol. 103, Issue 5
    • DOI: 10.1111/jace.16983

    Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
    journal, April 2019

    • Hoffmann, Jordan; Bar-Sinai, Yohai; Lee, Lisa M.
    • Science Advances, Vol. 5, Issue 4
    • DOI: 10.1126/sciadv.aau6792

    Machine learning for data-driven discovery in solid Earth geoscience
    journal, March 2019

    • Bergen, Karianne J.; Johnson, Paul A.; de Hoop, Maarten V.
    • Science, Vol. 363, Issue 6433
    • DOI: 10.1126/science.aau0323

    Crystal symmetry determination in electron diffraction using machine learning
    journal, January 2020

    • Kaufmann, Kevin; Zhu, Chaoyi; Rosengarten, Alexander S.
    • Science, Vol. 367, Issue 6477
    • DOI: 10.1126/science.aay3062

    Prediction of new iodine-containing apatites using machine learning and density functional theory
    journal, August 2019

    • Hartnett, Timothy Q.; Ayyasamy, Mukil V.; Balachandran, Prasanna V.
    • MRS Communications, Vol. 9, Issue 3
    • DOI: 10.1557/mrc.2019.103

    Data-centric science for materials innovation
    journal, September 2018

    • Tanaka, Isao; Rajan, Krishna; Wolverton, Christopher
    • MRS Bulletin, Vol. 43, Issue 9
    • DOI: 10.1557/mrs.2018.205

    Artificial intelligence for materials discovery
    journal, July 2019

    • Gomes, Carla P.; Selman, Bart; Gregoire, John M.
    • MRS Bulletin, Vol. 44, Issue 7
    • DOI: 10.1557/mrs.2019.158

    Progress toward autonomous experimental systems for alloy development
    journal, April 2019


    Review on Quantum Mechanically Guided Design of Ultra-Strong Metallic Glasses
    journal, April 2020