Adaptive strategies for materials design using uncertainties

Balachandran, Prasanna V.; Xue, Dezhen; Theiler, James; Hogden, John; Lookman, Turab

doi:10.1038/srep19660

Title: Adaptive strategies for materials design using uncertainties

Abstract

Here, we compare several adaptive design strategies using a data set of 223 M2AX family of compounds for which the elastic properties [bulk (B), shear (G), and Young’s (E) modulus] have been computed using density functional theory. The design strategies are decomposed into an iterative loop with two main steps: machine learning is used to train a regressor that predicts elastic properties in terms of elementary orbital radii of the individual components of the materials; and a selector uses these predictions and their uncertainties to choose the next material to investigate. The ultimate goal is to obtain a material with desired elastic properties in as few iterations as possible. We examine how the choice of data set size, regressor and selector impact the design. We find that selectors that use information about the prediction uncertainty outperform those that don’t. Our work is a step in illustrating how adaptive design tools can guide the search for new materials with desired properties.

Authors:

Balachandran, Prasanna V. ^[1]; Xue, Dezhen ^[2]; Theiler, James ^[1]; Hogden, John ^[1]; Lookman, Turab ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Xi'an Jiaotong Univ., Xi'an (China)

Publication Date:: Thu Jan 21 00:00:00 EST 2016

Research Org.:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1259303

Grant/Contract Number:: 20140013DR

Resource Type:: Journal Article: Accepted Manuscript

Journal Name:: Scientific Reports

Additional Journal Information:: Journal Volume: 6; Journal ID: ISSN 2045-2322

Publisher:: Nature Publishing Group

Country of Publication:: United States

Language:: English

Subject:: 36 MATERIALS SCIENCE; coarse-grained models; computational methods

Citation Formats


                    Balachandran, Prasanna V., Xue, Dezhen, Theiler, James, Hogden, John, and Lookman, Turab. Adaptive strategies for materials design using uncertainties.  United States: N. p., 2016. 
        Web.  doi:10.1038/srep19660.

Copy to clipboard


                    Balachandran, Prasanna V., Xue, Dezhen, Theiler, James, Hogden, John, & Lookman, Turab. Adaptive strategies for materials design using uncertainties.  United States.  https://doi.org/10.1038/srep19660

Copy to clipboard


                    Balachandran, Prasanna V., Xue, Dezhen, Theiler, James, Hogden, John, and Lookman, Turab. 2016.  
        "Adaptive strategies for materials design using uncertainties".  United States.  https://doi.org/10.1038/srep19660.  https://www.osti.gov/servlets/purl/1259303.

Copy to clipboard


                    
@article{osti_1259303,

  title        = {Adaptive strategies for materials design using uncertainties},

  author       = {Balachandran, Prasanna V. and Xue, Dezhen and Theiler, James and Hogden, John and Lookman, Turab},

  abstractNote = {Here, we compare several adaptive design strategies using a data set of 223 M2AX family of compounds for which the elastic properties [bulk (B), shear (G), and Young’s (E) modulus] have been computed using density functional theory. The design strategies are decomposed into an iterative loop with two main steps: machine learning is used to train a regressor that predicts elastic properties in terms of elementary orbital radii of the individual components of the materials; and a selector uses these predictions and their uncertainties to choose the next material to investigate. The ultimate goal is to obtain a material with desired elastic properties in as few iterations as possible. We examine how the choice of data set size, regressor and selector impact the design. We find that selectors that use information about the prediction uncertainty outperform those that don’t. Our work is a step in illustrating how adaptive design tools can guide the search for new materials with desired properties.},

  doi          = {10.1038/srep19660},

  url          = {https://www.osti.gov/biblio/1259303},
  journal      = {Scientific Reports},

  issn         = {2045-2322},

  number       = ,

  volume       = 6,

  place        = {United States},

  year         = {Thu Jan 21 00:00:00 EST 2016},

  month        = {Thu Jan 21 00:00:00 EST 2016}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1038/srep19660

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 138 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

Neural Networks in Materials Science.
journal, January 1999

Bhadeshia, H. K. D. H.
ISIJ International, Vol. 39, Issue 10
https://doi.org/10.2355/isijinternational.39.966

Identifying the ‘inorganic gene’ for high-temperature piezoelectric perovskites through statistical learning
journal, February 2011

Balachandran, Prasanna V.; Broderick, Scott R.; Rajan, Krishna
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 467, Issue 2132
https://doi.org/10.1098/rspa.2010.0543

Data mining for materials: Computational experiments with $A B$ compounds
journal, March 2012

Saad, Yousef; Gao, Da; Ngo, Thanh
Physical Review B, Vol. 85, Issue 10
https://doi.org/10.1103/PhysRevB.85.104104

Accelerating materials property predictions using machine learning
journal, September 2013

Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun
Scientific Reports, Vol. 3, Issue 1
https://doi.org/10.1038/srep02810

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids
journal, February 2014

Seko, Atsuto; Maekawa, Tomoya; Tsuda, Koji
Physical Review B, Vol. 89, Issue 5
https://doi.org/10.1103/PhysRevB.89.054303

Informatics-aided bandgap engineering for solar materials
journal, February 2014

Dey, Partha; Bible, Joe; Datta, Somnath
Computational Materials Science, Vol. 83
https://doi.org/10.1016/j.commatsci.2013.10.016

The high-throughput highway to computational materials design
journal, February 2013

Curtarolo, Stefano; Hart, Gus L. W.; Nardelli, Marco Buongiorno
Nature Materials, Vol. 12, Issue 3
https://doi.org/10.1038/nmat3568

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

Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy
APL Materials, Vol. 1, Issue 1
https://doi.org/10.1063/1.4812323

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
journal, September 2013

Saal, James E.; Kirklin, Scott; Aykol, Muratahan
JOM, Vol. 65, Issue 11
https://doi.org/10.1007/s11837-013-0755-4

Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds
journal, March 2015

Gautier, Romain; Zhang, Xiuwen; Hu, Linhua
Nature Chemistry, Vol. 7, Issue 4
https://doi.org/10.1038/nchem.2207

Efficient Global Optimization of Expensive Black-Box Functions
journal, January 1998

Jones, Donald R.; Schonlau, Matthias; Welch, William J.
Journal of Global Optimization, Vol. 13, Issue 4, p. 455-492
https://doi.org/10.1023/A:1008306431147

The Illusion of Distribution-Free Small-Sample Classification in Genomics
journal, August 2011

R. Dougherty, Edward; Zollanvari, Amin; M. Braga-Neto, Ulisses
Current Genomics, Vol. 12, Issue 5
https://doi.org/10.2174/138920211796429763

Probabilistic machine learning and artificial intelligence
journal, May 2015

Ghahramani, Zoubin
Nature, Vol. 521, Issue 7553
https://doi.org/10.1038/nature14541

The Knowledge-Gradient Policy for Correlated Normal Beliefs
journal, November 2009

Frazier, Peter; Powell, Warren; Dayanik, Savas
INFORMS Journal on Computing, Vol. 21, Issue 4
https://doi.org/10.1287/ijoc.1080.0314

Recent advances in surrogate-based optimization
journal, January 2009

Forrester, Alexander I. J.; Keane, Andy J.
Progress in Aerospace Sciences, Vol. 45, Issue 1-3
https://doi.org/10.1016/j.paerosci.2008.11.001

Gaussian Processes for Machine Learning
book, January 2005

Rasmussen, Carl Edward; Williams, Christopher K. I.
The MIT Press
https://doi.org/10.7551/mitpress/3206.001.0001

Elastic and Mechanical Properties of the MAX Phases
journal, August 2011

Barsoum, Michel W.; Radovic, Miladin
Annual Review of Materials Research, Vol. 41, Issue 1
https://doi.org/10.1146/annurev-matsci-062910-100448

A genomic approach to the stability, elastic, and electronic properties of the MAX phases: A genomic approach to stability and properties of the MAX phases
journal, June 2014

Aryal, Sitaram; Sakidja, Ridwan; Barsoum, Michel W.
physica status solidi (b), Vol. 251, Issue 8
https://doi.org/10.1002/pssb.201451226

Orbital Radii of Atoms and Ions
journal, June 1965

Waber, J. T.; Cromer, Don T.
The Journal of Chemical Physics, Vol. 42, Issue 12
https://doi.org/10.1063/1.1695904

Extreme Poisson's ratios and their electronic origin in B2 CsCl-type AB intermetallic compounds
journal, April 2012

Wang, X. F.; Jones, Travis E.; Li, W.
Physical Review B, Vol. 85, Issue 13
https://doi.org/10.1103/PhysRevB.85.134108

Influence of the electronic structure on the ductile behavior of B2 CsCl-type AB intermetallics
journal, November 2009

Gschneidner Jr., K. A.; Ji, Min; Wang, C. Z.
Acta Materialia, Vol. 57, Issue 19
https://doi.org/10.1016/j.actamat.2009.08.012

Materials Prediction via Classification Learning
journal, August 2015

Balachandran, Prasanna V.; Theiler, James; Rondinelli, James M.
Scientific Reports, Vol. 5, Issue 1
https://doi.org/10.1038/srep13285

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

Meredig, B.; Agrawal, A.; Kirklin, S.
Physical Review B, Vol. 89, Issue 9
https://doi.org/10.1103/PhysRevB.89.094104

Crystal structure representations for machine learning models of formation energies
journal, April 2015

Faber, Felix; Lindmaa, Alexander; von Lilienfeld, O. Anatole
International Journal of Quantum Chemistry, Vol. 115, Issue 16
https://doi.org/10.1002/qua.24917

Machine Learning in Materials Science
book, January 2016

Mueller, Tim; Kusne, Aaron Gilad; Ramprasad, Rampi
Reviews in Computational Chemistry, Vol. 29
https://doi.org/10.1002/9781119148739.ch4

Materials Informatics: The Materials “Gene” and Big Data
journal, July 2015

Rajan, Krishna
Annual Review of Materials Research, Vol. 45, Issue 1
https://doi.org/10.1146/annurev-matsci-070214-021132

Materials Data Science: Current Status and Future Outlook
journal, July 2015

Kalidindi, Surya R.; De Graef, Marc
Annual Review of Materials Research, Vol. 45, Issue 1
https://doi.org/10.1146/annurev-matsci-070214-020844

A predictive machine learning approach for microstructure optimization and materials design
journal, June 2015

Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang
Scientific Reports, Vol. 5, Issue 1
https://doi.org/10.1038/srep11551

Big–deep–smart data in imaging for guiding materials design
journal, September 2015

Kalinin, Sergei V.; Sumpter, Bobby G.; Archibald, Richard K.
Nature Materials, Vol. 14, Issue 10
https://doi.org/10.1038/nmat4395

On Outliers and Activity CliffsWhy QSAR Often Disappoints
journal, July 2006

Maggiora, Gerald M.
Journal of Chemical Information and Modeling, Vol. 46, Issue 4
https://doi.org/10.1021/ci060117s

Voyages to the (un)known: adaptive design of bioactive compounds
journal, January 2009

Schneider, Gisbert; Hartenfeller, Markus; Reutlinger, Michael
Trends in Biotechnology, Vol. 27, Issue 1
https://doi.org/10.1016/j.tibtech.2008.09.005

Charge-Density-Shear-Moduli Relationships in Aluminum-Lithium Alloys
text, January 2001

Eberhart, Mark
The American Physical Society
https://doi.org/10.17877/de290r-11383

Crystal structure representations for machine learning models of formation energies
text, January 2015

Felix, Faber,; Alexander, Lindmaa,; Anatole, von Lilienfeld, O.
Wiley
https://doi.org/10.5451/unibas-ep53264

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids
text, January 2013

Seko, Atsuto; Maekawa, Tomoya; Tsuda, Koji
arXiv
https://doi.org/10.48550/arxiv.1310.1546

Voyages to the (un)known: adaptive design of bioactive compounds
journal, January 2009

Schneider, Gisbert; Hartenfeller, Markus; Reutlinger, Michael
Trends in Biotechnology, Vol. 27, Issue 1
https://doi.org/10.1016/j.tibtech.2008.09.005

Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs
journal, February 2008

Guha, Rajarshi; Van Drie, John H.
Journal of Chemical Information and Modeling, Vol. 48, Issue 3
https://doi.org/10.1021/ci7004093

Probabilistic machine learning and artificial intelligence
journal, May 2015

Ghahramani, Zoubin
Nature, Vol. 521, Issue 7553
https://doi.org/10.1038/nature14541

The high-throughput highway to computational materials design
journal, February 2013

Curtarolo, Stefano; Hart, Gus L. W.; Nardelli, Marco Buongiorno
Nature Materials, Vol. 12, Issue 3
https://doi.org/10.1038/nmat3568

A predictive machine learning approach for microstructure optimization and materials design
journal, June 2015

Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang
Scientific Reports, Vol. 5, Issue 1
https://doi.org/10.1038/srep11551

A comprehensive survey of M ₂ AX phase elastic properties
journal, July 2009

Cover, M. F.; Warschkow, O.; Bilek, M. M. M.
Journal of Physics: Condensed Matter, Vol. 21, Issue 30
https://doi.org/10.1088/0953-8984/21/30/305403

Charge-Density-Shear-Moduli Relationships in Aluminum-Lithium Alloys
journal, October 2001

Eberhart, Mark
Physical Review Letters, Vol. 87, Issue 20
https://doi.org/10.1103/physrevlett.87.205503

The Knowledge-Gradient Policy for Correlated Normal Beliefs
journal, November 2009

Frazier, Peter; Powell, Warren; Dayanik, Savas
INFORMS Journal on Computing, Vol. 21, Issue 4
https://doi.org/10.1287/ijoc.1080.0314

The Illusion of Distribution-Free Small-Sample Classification in Genomics
journal, August 2011

R. Dougherty, Edward; Zollanvari, Amin; M. Braga-Neto, Ulisses
Current Genomics, Vol. 12, Issue 5
https://doi.org/10.2174/138920211796429763

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids
text, January 2013

Seko, Atsuto; Maekawa, Tomoya; Tsuda, Koji
arXiv
https://doi.org/10.48550/arxiv.1310.1546

Works referencing / citing this record:

Accelerated Discovery of Large Electrostrains in BaTiO ₃ -Based Piezoelectrics Using Active Learning
journal, January 2018

Yuan, Ruihao; Liu, Zhen; Balachandran, Prasanna V.
Advanced Materials, Vol. 30, Issue 7
https://doi.org/10.1002/adma.201702884

Accelerated Search for BaTiO ₃ ‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design
journal, August 2019

Yuan, Ruihao; Tian, Yuan; Xue, Dezhen
Advanced Science, Vol. 6, Issue 21
https://doi.org/10.1002/advs.201901395

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

Chen, Chi; Zuo, Yunxing; Ye, Weike
Advanced Energy Materials, Vol. 10, Issue 8
https://doi.org/10.1002/aenm.201903242

Material data mining in Nianyi Chen's scientific family: Material data mining in Nianyi Chen's scientific family
journal, February 2018

Lu, Wencong; Lv, Wei; Zhang, Qing
Journal of Chemometrics, Vol. 32, Issue 11
https://doi.org/10.1002/cem.3022

Toward a Predictive Hierarchical Multiscale Modeling Approach for Energetic Materials
book, February 2019

Barnes, Brian C.; Brennan, John K.; Byrd, Edward F. C.
Computational Approaches for Chemistry Under Extreme Conditions
https://doi.org/10.1007/978-3-030-05600-1_10

Importance of Feature Selection in Machine Learning and Adaptive Design for Materials
book, January 2018

Balachandran, Prasanna V.; Xue, Dezhen; Theiler, James
Materials Discovery and Design
https://doi.org/10.1007/978-3-319-99465-9_3

Filtering Bayesian optimization approach in weakly specified search space
journal, July 2018

Nguyen, Vu; Gupta, Sunil; Rana, Santu
Knowledge and Information Systems, Vol. 60, Issue 1
https://doi.org/10.1007/s10115-018-1238-2

Materials informatics
journal, January 2018

Ramakrishna, Seeram; Zhang, Tong-Yi; Lu, Wen-Cong
Journal of Intelligent Manufacturing, Vol. 30, Issue 6
https://doi.org/10.1007/s10845-018-1392-0

Data Infrastructure Elements in Support of Accelerated Materials Innovation: ELA, PyMKS, and MATIN
journal, October 2019

Kalidindi, Surya R.; Khosravani, Ali; Yucel, Berkay
Integrating Materials and Manufacturing Innovation, Vol. 8, Issue 4
https://doi.org/10.1007/s40192-019-00156-1

Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design
journal, January 2020

Honarmandi, Pejman; Arróyave, Raymundo
Integrating Materials and Manufacturing Innovation, Vol. 9, Issue 1
https://doi.org/10.1007/s40192-020-00168-2

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
https://doi.org/10.1038/s41524-019-0153-8

Recent advances and applications of machine learning in solid-state materials science
journal, August 2019

Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana
npj Computational Materials, Vol. 5, Issue 1
https://doi.org/10.1038/s41524-019-0221-0

Rapid Bayesian optimisation for synthesis of short polymer fiber materials
journal, July 2017

Li, Cheng; Rubín de Celis Leal, David; Rana, Santu
Scientific Reports, Vol. 7, Issue 1
https://doi.org/10.1038/s41598-017-05723-0

Multi-objective Optimization for Materials Discovery via Adaptive Design
journal, February 2018

Gopakumar, Abhijith M.; Balachandran, Prasanna V.; Xue, Dezhen
Scientific Reports, Vol. 8, Issue 1
https://doi.org/10.1038/s41598-018-21936-3

Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application
journal, April 2018

Jalem, Randy; Kanamori, Kenta; Takeuchi, Ichiro
Scientific Reports, Vol. 8, Issue 1
https://doi.org/10.1038/s41598-018-23852-y

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

Noack, Marcus M.; Yager, Kevin G.; Fukuto, Masafumi
Scientific Reports, Vol. 9, Issue 1
https://doi.org/10.1038/s41598-019-48114-3

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
journal, October 2016

de Jong, Maarten; Chen, Wei; Notestine, Randy
Scientific Reports, Vol. 6, Issue 1
https://doi.org/10.1038/srep34256

Structure prediction of boron-doped graphene by machine learning
journal, March 2018

M. Dieb, Thaer; Hou, Zhufeng; Tsuda, Koji
The Journal of Chemical Physics, Vol. 148, Issue 24
https://doi.org/10.1063/1.5018065

Accelerated search for BaTiO ₃ -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning
journal, November 2016

Xue, Dezhen; Balachandran, Prasanna V.; Yuan, Ruihao
Proceedings of the National Academy of Sciences, Vol. 113, Issue 47
https://doi.org/10.1073/pnas.1607412113

Rocketsled: a software library for optimizing high-throughput computational searches
journal, April 2019

Dunn, Alexander; Brenneck, Julien; Jain, Anubhav
Journal of Physics: Materials, Vol. 2, Issue 3
https://doi.org/10.1088/2515-7639/ab0c3d

Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles
journal, January 2020

Kadupitiya, Jcs; Fox, Geoffrey C.; Jadhao, Vikram
The International Journal of High Performance Computing Applications, Vol. 34, Issue 3
https://doi.org/10.1177/1094342019899457

Bayesian optimization for conformer generation
journal, May 2019

Chan, Lucian; Hutchison, Geoffrey R.; Morris, Garrett M.
Journal of Cheminformatics, Vol. 11, Issue 1
https://doi.org/10.1186/s13321-019-0354-7

Bayesian optimization for computationally extensive probability distributions
journal, March 2018

Tamura, Ryo; Hukushima, Koji
PLOS ONE, Vol. 13, Issue 3
https://doi.org/10.1371/journal.pone.0193785

Finding New Perovskite Halides via Machine Learning
journal, April 2016

Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho
Frontiers in Materials, Vol. 3
https://doi.org/10.3389/fmats.2016.00019

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges
journal, January 2020

Chen, Guang; Shen, Zhiqiang; Iyer, Akshay
Polymers, Vol. 12, Issue 1
https://doi.org/10.3390/polym12010163

MDTS: automatic complex materials design using Monte Carlo tree search
text, January 2017

Dieb, Thaer M.; Ju, Shenghong; Yoshizoe, Kazuki
Taylor & Francis
https://doi.org/10.6084/m9.figshare.5224558

MDTS: automatic complex materials design using Monte Carlo tree search
journal, July 2017

M. Dieb, Thaer; Ju, Shenghong; Yoshizoe, Kazuki
Science and Technology of Advanced Materials, Vol. 18, Issue 1
https://doi.org/10.1080/14686996.2017.1344083

Materials informatics
journal, April 2009

Rodgers, John
Materials Science and Technology, Vol. 25, Issue 4
https://doi.org/10.1179/174328409x430456

Bayesian Optimization for Conformer Generation
posted_content, October 2018

Chan, Lucian; Hutchison, Geoffrey; Morris, Garrett
ChemRxiv
https://doi.org/10.26434/chemrxiv.7228940.v3

Materials Informatics
journal, April 2009

Rajan, Krishna; Mendez, Patricio
Statistical Analysis and Data Mining, Vol. 1, Issue 5
https://doi.org/10.1002/sam.10022

Bayesian Optimization for Conformer Generation
posted_content, February 2019

Chan, Lucian; Hutchison, Geoffrey; Morris, Garrett
ChemRxiv
https://doi.org/10.26434/chemrxiv.7228940

Bayesian Optimization for Conformer Generation
posted_content, October 2018

Chan, Lucian; Hutchison, Geoffrey; Morris, Garrett
ChemRxiv
https://doi.org/10.26434/chemrxiv.7228940.v2

Machine Learning for Parameter Auto-tuning in Molecular Dynamics Simulations: Efficient Dynamics of Ions near Polarizable Nanoparticles
text, January 2019

Kadupitiya, Jcs; Fox, Geoffrey C.; Jadhao, Vikram
Unpublished
https://doi.org/10.13140/rg.2.2.17663.76965

Extensive deep neural networks for transferring small scale learning to large scale systems
text, January 2017

Mills, Kyle; Ryczko, Kevin; Luchak, Iryna
arXiv
https://doi.org/10.48550/arxiv.1708.06686

Rapid Bayesian optimisation for synthesis of short polymer fiber materials
preprint, January 2018

Li, Cheng; Leal, David Rubin de Celis; Rana, Santu
arXiv
https://doi.org/10.48550/arxiv.1802.05841

Autonomous Efficient Experiment Design for Materials Discovery with Bayesian Model Averaging
text, January 2018

Talapatra, Anjana; Boluki, Shahin; Duong, Thien
arXiv
https://doi.org/10.48550/arxiv.1803.05460

MDTS: automatic complex materials design using Monte Carlo tree search
text, January 2017

Dieb, Thaer M.; Ju, Shenghong; Yoshizoe, Kazuki
Taylor & Francis
https://doi.org/10.6084/m9.figshare.5224558.v1

Multi-objective Optimization for Materials Discovery via Adaptive Design
journal, February 2018

Gopakumar, Abhijith M.; Balachandran, Prasanna V.; Xue, Dezhen
Scientific Reports, Vol. 8, Issue 1
https://doi.org/10.1038/s41598-018-21936-3

Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application
journal, April 2018

Jalem, Randy; Kanamori, Kenta; Takeuchi, Ichiro
Scientific Reports, Vol. 8, Issue 1
https://doi.org/10.1038/s41598-018-23852-y

Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy
journal, September 2018

Kiyohara, Shin; Miyata, Tomohiro; Tsuda, Koji
Scientific Reports, Vol. 8, Issue 1
https://doi.org/10.1038/s41598-018-30994-6

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

Noack, Marcus M.; Yager, Kevin G.; Fukuto, Masafumi
Scientific Reports, Vol. 9, Issue 1
https://doi.org/10.1038/s41598-019-48114-3

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments
journal, January 2020

Noack, Marcus M.; Doerk, Gregory S.; Li, Ruipeng
Scientific Reports, Vol. 10, Issue 1
https://doi.org/10.1038/s41598-020-57887-x

Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables
journal, March 2020

Zhang, Yichi; Apley, Daniel W.; Chen, Wei
Scientific Reports, Vol. 10, Issue 1
https://doi.org/10.1038/s41598-020-60652-9

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
journal, October 2016

de Jong, Maarten; Chen, Wei; Notestine, Randy
Scientific Reports, Vol. 6, Issue 1
https://doi.org/10.1038/srep34256

Finding New Perovskite Halides via Machine Learning
journal, April 2016

Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho
Frontiers in Materials, Vol. 3
https://doi.org/10.3389/fmats.2016.00019

Bayesian optimization for computationally extensive probability distributions
text, January 2018

Tamura, Ryo; Hukushima, Koji
arXiv
https://doi.org/10.48550/arxiv.1803.04577

MDTS: automatic complex materials design using Monte Carlo tree search
text, January 2017

Dieb, Thaer M.; Ju, Shenghong; Yoshizoe, Kazuki
Taylor & Francis
https://doi.org/10.6084/m9.figshare.5224558

Similar records in OSTI.GOV collections:

Physics-based adaptivity of a spectral method for the Vlasov–Poisson equations based on the asymmetrically-weighted Hermite expansion in velocity space

Journal Article Pagliantini, Cecilia; Delzanno, Gian; Markidis, Stefano - Journal of Computational Physics

We propose a spectral method for the 1D-1V Vlasov–Poisson system where the discretization in velocity space is based on asymmetrically-weighted Hermite functions, dynamically adapted via a scaling α and shifting u of the velocity variable. Specifically, at each time instant an adaptivity criterion selects new values of α and u based on the numerical solution of the discrete Vlasov–Poisson system obtained at that time step. Once the new values of the Hermite parameters α and u are fixed, the Hermite expansion is updated and the discrete system is further evolved for the next time step. The procedure is applied iterativelymore »« less
https://doi.org/10.1016/j.jcp.2023.112252

Full Text Available
Adaptive scapula bone remodeling computational simulation: Relevance to regenerative medicine

Journal Article

Shoulder arthroplasty success has been attributed to many factors including, bone quality, soft tissue balancing, surgeon experience, and implant design. Improved long-term success is primarily limited by glenoid implant loosening. Prosthesis design examines materials and shape and determines whether the design should withstand a lifetime of use. Finite element (FE) analyses have been extensively used to study stresses and strains produced in implants and bone. However, these static analyses only measure a moment in time and not the adaptive response to the altered environment produced by the therapeutic intervention. Computational analyses that integrate remodeling rules predict how bone will respondmore »« less
https://doi.org/10.1016/J.JCP.2012.09.028
Progress on Demonstration of a MOOSE-Based Coupled Capability for Hot Channel Factors in Fast Reactors

Technical Report Park, Hansol; Yu, Yiqi; Shemon, Emily; ...

Hot channel factors (HCFs) are computed values that account for the impact on predicted peak fuel, cladding, and coolant temperatures due to uncertainties in the as-built reactor’s material properties and geometry as well as uncertainties due to modeling approximations. Reduction in computed HCF values via reduction or elimination of modeling approximations may translate to significant economic savings if the reactor power can be raised due to the extra temperature margin gained. While limited historical datasets exist for sodium-cooled fast reactors (SFRs), there are no available HCF data for lead-cooled fast reactors (LFRs) outside of work generated previously within NEAMS. Themore »« less
https://doi.org/10.2172/1888753

Full Text Available
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

Journal Article Ahmad, Zeeshan; Xie, Tian; Maheshwari, Chinmay; ... - ACS Central Science

Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12 000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining themore »« less
Cited by 114
https://doi.org/10.1021/acscentsci.8b00229
Convergence of a discretized self-adaptive evolutionary algorithm on multi-dimensional problems.

Journal Article Hart, William; DeLaurentis, John - Proposed for publication in IEEE Transactions on Evolutionary Computation.

We consider the convergence properties of a non-elitist self-adaptive evolutionary strategy (ES) on multi-dimensional problems. In particular, we apply our recent convergence theory for a discretized (1,{lambda})-ES to design a related (1,{lambda})-ES that converges on a class of seperable, unimodal multi-dimensional problems. The distinguishing feature of self-adaptive evolutionary algorithms (EAs) is that the control parameters (like mutation step lengths) are evolved by the evolutionary algorithm. Thus the control parameters are adapted in an implicit manner that relies on the evolutionary dynamics to ensure that more effective control parameters are propagated during the search. Self-adaptation is a central feature of EAsmore »« less

Similar Records

Title: Adaptive strategies for materials design using uncertainties

Abstract

Citation Formats

Neural Networks in Materials Science. journal, January 1999

Identifying the ‘inorganic gene’ for high-temperature piezoelectric perovskites through statistical learning journal, February 2011

Data mining for materials: Computational experiments with A B compounds journal, March 2012

Accelerating materials property predictions using machine learning journal, September 2013

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids journal, February 2014

Informatics-aided bandgap engineering for solar materials journal, February 2014

The high-throughput highway to computational materials design journal, February 2013

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

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) journal, September 2013

Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds journal, March 2015

Efficient Global Optimization of Expensive Black-Box Functions journal, January 1998

The Illusion of Distribution-Free Small-Sample Classification in Genomics journal, August 2011

Probabilistic machine learning and artificial intelligence journal, May 2015

The Knowledge-Gradient Policy for Correlated Normal Beliefs journal, November 2009

Recent advances in surrogate-based optimization journal, January 2009

Gaussian Processes for Machine Learning book, January 2005

Elastic and Mechanical Properties of the MAX Phases journal, August 2011

A genomic approach to the stability, elastic, and electronic properties of the MAX phases: A genomic approach to stability and properties of the MAX phases journal, June 2014

Orbital Radii of Atoms and Ions journal, June 1965

Extreme Poisson's ratios and their electronic origin in B2 CsCl-type AB intermetallic compounds journal, April 2012

Influence of the electronic structure on the ductile behavior of B2 CsCl-type AB intermetallics journal, November 2009

Materials Prediction via Classification Learning journal, August 2015

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

Crystal structure representations for machine learning models of formation energies journal, April 2015

Machine Learning in Materials Science book, January 2016

Materials Informatics: The Materials “Gene” and Big Data journal, July 2015

Materials Data Science: Current Status and Future Outlook journal, July 2015

A predictive machine learning approach for microstructure optimization and materials design journal, June 2015

Big–deep–smart data in imaging for guiding materials design journal, September 2015

On Outliers and Activity CliffsWhy QSAR Often Disappoints journal, July 2006

Voyages to the (un)known: adaptive design of bioactive compounds journal, January 2009

Charge-Density-Shear-Moduli Relationships in Aluminum-Lithium Alloys text, January 2001

Crystal structure representations for machine learning models of formation energies text, January 2015

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids text, January 2013

Voyages to the (un)known: adaptive design of bioactive compounds journal, January 2009

Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs journal, February 2008

Probabilistic machine learning and artificial intelligence journal, May 2015

The high-throughput highway to computational materials design journal, February 2013

A predictive machine learning approach for microstructure optimization and materials design journal, June 2015

A comprehensive survey of M 2 AX phase elastic properties journal, July 2009

Charge-Density-Shear-Moduli Relationships in Aluminum-Lithium Alloys journal, October 2001

The Knowledge-Gradient Policy for Correlated Normal Beliefs journal, November 2009

The Illusion of Distribution-Free Small-Sample Classification in Genomics journal, August 2011

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids text, January 2013

Accelerated Discovery of Large Electrostrains in BaTiO 3 -Based Piezoelectrics Using Active Learning journal, January 2018

Accelerated Search for BaTiO 3 ‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design journal, August 2019

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

Material data mining in Nianyi Chen's scientific family: Material data mining in Nianyi Chen's scientific family journal, February 2018

Toward a Predictive Hierarchical Multiscale Modeling Approach for Energetic Materials book, February 2019

Importance of Feature Selection in Machine Learning and Adaptive Design for Materials book, January 2018

Filtering Bayesian optimization approach in weakly specified search space journal, July 2018

Materials informatics journal, January 2018

Data Infrastructure Elements in Support of Accelerated Materials Innovation: ELA, PyMKS, and MATIN journal, October 2019

Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design journal, January 2020

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

Recent advances and applications of machine learning in solid-state materials science journal, August 2019

Rapid Bayesian optimisation for synthesis of short polymer fiber materials journal, July 2017

Multi-objective Optimization for Materials Discovery via Adaptive Design journal, February 2018

Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application journal, April 2018

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

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds journal, October 2016

Structure prediction of boron-doped graphene by machine learning journal, March 2018

Accelerated search for BaTiO 3 -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning journal, November 2016

Rocketsled: a software library for optimizing high-throughput computational searches journal, April 2019

Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles journal, January 2020

Bayesian optimization for conformer generation journal, May 2019

Bayesian optimization for computationally extensive probability distributions journal, March 2018

Finding New Perovskite Halides via Machine Learning journal, April 2016

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges journal, January 2020

MDTS: automatic complex materials design using Monte Carlo tree search text, January 2017

MDTS: automatic complex materials design using Monte Carlo tree search journal, July 2017

Materials informatics journal, April 2009

Bayesian Optimization for Conformer Generation posted_content, October 2018

Materials Informatics journal, April 2009

Bayesian Optimization for Conformer Generation posted_content, February 2019

Bayesian Optimization for Conformer Generation posted_content, October 2018

Machine Learning for Parameter Auto-tuning in Molecular Dynamics Simulations: Efficient Dynamics of Ions near Polarizable Nanoparticles text, January 2019

Neural Networks in Materials Science.
journal, January 1999

Identifying the ‘inorganic gene’ for high-temperature piezoelectric perovskites through statistical learning
journal, February 2011

Data mining for materials: Computational experiments with $A B$ compounds
journal, March 2012

Accelerating materials property predictions using machine learning
journal, September 2013

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids
journal, February 2014

Informatics-aided bandgap engineering for solar materials
journal, February 2014

The high-throughput highway to computational materials design
journal, February 2013

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

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
journal, September 2013

Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds
journal, March 2015

Efficient Global Optimization of Expensive Black-Box Functions
journal, January 1998

The Illusion of Distribution-Free Small-Sample Classification in Genomics
journal, August 2011

Probabilistic machine learning and artificial intelligence
journal, May 2015

The Knowledge-Gradient Policy for Correlated Normal Beliefs
journal, November 2009

Recent advances in surrogate-based optimization
journal, January 2009

Gaussian Processes for Machine Learning
book, January 2005

Elastic and Mechanical Properties of the MAX Phases
journal, August 2011

A genomic approach to the stability, elastic, and electronic properties of the MAX phases: A genomic approach to stability and properties of the MAX phases
journal, June 2014

Orbital Radii of Atoms and Ions
journal, June 1965

Extreme Poisson's ratios and their electronic origin in B2 CsCl-type AB intermetallic compounds
journal, April 2012

Influence of the electronic structure on the ductile behavior of B2 CsCl-type AB intermetallics
journal, November 2009

Materials Prediction via Classification Learning
journal, August 2015

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

Crystal structure representations for machine learning models of formation energies
journal, April 2015

Machine Learning in Materials Science
book, January 2016

Materials Informatics: The Materials “Gene” and Big Data
journal, July 2015

Materials Data Science: Current Status and Future Outlook
journal, July 2015

A predictive machine learning approach for microstructure optimization and materials design
journal, June 2015

Big–deep–smart data in imaging for guiding materials design
journal, September 2015

On Outliers and Activity CliffsWhy QSAR Often Disappoints
journal, July 2006

Voyages to the (un)known: adaptive design of bioactive compounds
journal, January 2009

Charge-Density-Shear-Moduli Relationships in Aluminum-Lithium Alloys
text, January 2001

Crystal structure representations for machine learning models of formation energies
text, January 2015

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids
text, January 2013

Voyages to the (un)known: adaptive design of bioactive compounds
journal, January 2009

Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs
journal, February 2008

Probabilistic machine learning and artificial intelligence
journal, May 2015

The high-throughput highway to computational materials design
journal, February 2013

A predictive machine learning approach for microstructure optimization and materials design
journal, June 2015

A comprehensive survey of M ₂ AX phase elastic properties
journal, July 2009

Charge-Density-Shear-Moduli Relationships in Aluminum-Lithium Alloys
journal, October 2001

The Knowledge-Gradient Policy for Correlated Normal Beliefs
journal, November 2009

The Illusion of Distribution-Free Small-Sample Classification in Genomics
journal, August 2011

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids
text, January 2013

Accelerated Discovery of Large Electrostrains in BaTiO ₃ -Based Piezoelectrics Using Active Learning
journal, January 2018

Accelerated Search for BaTiO ₃ ‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design
journal, August 2019

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

Material data mining in Nianyi Chen's scientific family: Material data mining in Nianyi Chen's scientific family
journal, February 2018

Toward a Predictive Hierarchical Multiscale Modeling Approach for Energetic Materials
book, February 2019

Importance of Feature Selection in Machine Learning and Adaptive Design for Materials
book, January 2018

Filtering Bayesian optimization approach in weakly specified search space
journal, July 2018

Materials informatics
journal, January 2018

Data Infrastructure Elements in Support of Accelerated Materials Innovation: ELA, PyMKS, and MATIN
journal, October 2019

Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design
journal, January 2020

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

Recent advances and applications of machine learning in solid-state materials science
journal, August 2019

Rapid Bayesian optimisation for synthesis of short polymer fiber materials
journal, July 2017

Multi-objective Optimization for Materials Discovery via Adaptive Design
journal, February 2018

Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application
journal, April 2018

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

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
journal, October 2016

Structure prediction of boron-doped graphene by machine learning
journal, March 2018

Accelerated search for BaTiO ₃ -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning
journal, November 2016

Rocketsled: a software library for optimizing high-throughput computational searches
journal, April 2019

Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles
journal, January 2020

Bayesian optimization for conformer generation
journal, May 2019

Bayesian optimization for computationally extensive probability distributions
journal, March 2018

Finding New Perovskite Halides via Machine Learning
journal, April 2016

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges
journal, January 2020

MDTS: automatic complex materials design using Monte Carlo tree search
text, January 2017

MDTS: automatic complex materials design using Monte Carlo tree search
journal, July 2017

Materials informatics
journal, April 2009

Bayesian Optimization for Conformer Generation
posted_content, October 2018

Materials Informatics
journal, April 2009

Bayesian Optimization for Conformer Generation
posted_content, February 2019

Bayesian Optimization for Conformer Generation
posted_content, October 2018

Machine Learning for Parameter Auto-tuning in Molecular Dynamics Simulations: Efficient Dynamics of Ions near Polarizable Nanoparticles
text, January 2019

Extensive deep neural networks for transferring small scale learning to large scale systems
text, January 2017

Rapid Bayesian optimisation for synthesis of short polymer fiber materials
preprint, January 2018

Autonomous Efficient Experiment Design for Materials Discovery with Bayesian Model Averaging
text, January 2018