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:
-
- 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:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1259303
- Grant/Contract Number:
- 20140013DR
- Resource Type:
- 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.
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
Balachandran, Prasanna V., Xue, Dezhen, Theiler, James, Hogden, John, and Lookman, Turab. Thu .
"Adaptive strategies for materials design using uncertainties". United States. https://doi.org/10.1038/srep19660. https://www.osti.gov/servlets/purl/1259303.
@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},
journal = {Scientific Reports},
number = ,
volume = 6,
place = {United States},
year = {Thu Jan 21 00:00:00 EST 2016},
month = {Thu Jan 21 00:00:00 EST 2016}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Cited by: 138 works
Citation information provided by
Web of Science
Web of Science
Save to My Library
You must Sign In or Create an Account in order to save documents to your 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
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
Data mining for materials: Computational experiments with compounds
journal, March 2012
- Saad, Yousef; Gao, Da; Ngo, Thanh
- Physical Review B, Vol. 85, Issue 10
Accelerating materials property predictions using machine learning
journal, September 2013
- Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun
- Scientific Reports, Vol. 3, Issue 1
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
Informatics-aided bandgap engineering for solar materials
journal, February 2014
- Dey, Partha; Bible, Joe; Datta, Somnath
- Computational Materials Science, Vol. 83
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
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
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
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
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
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
Probabilistic machine learning and artificial intelligence
journal, May 2015
- Ghahramani, Zoubin
- Nature, Vol. 521, Issue 7553
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
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
Gaussian Processes for Machine Learning
book, January 2005
- Rasmussen, Carl Edward; Williams, Christopher K. I.
- The MIT Press
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
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
Orbital Radii of Atoms and Ions
journal, June 1965
- Waber, J. T.; Cromer, Don T.
- The Journal of Chemical Physics, Vol. 42, Issue 12
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
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
Materials Prediction via Classification Learning
journal, August 2015
- Balachandran, Prasanna V.; Theiler, James; Rondinelli, James M.
- Scientific Reports, Vol. 5, Issue 1
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
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
Machine Learning in Materials Science
book, January 2016
- Mueller, Tim; Kusne, Aaron Gilad; Ramprasad, Rampi
- Reviews in Computational Chemistry, Vol. 29
Materials Informatics: The Materials “Gene” and Big Data
journal, July 2015
- Rajan, Krishna
- Annual Review of Materials Research, Vol. 45, Issue 1
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
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
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
On Outliers and Activity CliffsWhy QSAR Often Disappoints
journal, July 2006
- Maggiora, Gerald M.
- Journal of Chemical Information and Modeling, Vol. 46, Issue 4
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
Charge-Density-Shear-Moduli Relationships in Aluminum-Lithium Alloys
text, January 2001
- Eberhart, Mark
- The American Physical Society
Crystal structure representations for machine learning models of formation energies
text, January 2015
- Felix, Faber,; Alexander, Lindmaa,; Anatole, von Lilienfeld, O.
- Wiley
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
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
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
Probabilistic machine learning and artificial intelligence
journal, May 2015
- Ghahramani, Zoubin
- Nature, Vol. 521, Issue 7553
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
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
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
Charge-Density-Shear-Moduli Relationships in Aluminum-Lithium Alloys
journal, October 2001
- Eberhart, Mark
- Physical Review Letters, Vol. 87, Issue 20
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
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
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
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
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
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
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
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
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
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
Materials informatics
journal, January 2018
- Ramakrishna, Seeram; Zhang, Tong-Yi; Lu, Wen-Cong
- Journal of Intelligent Manufacturing, Vol. 30, Issue 6
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
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
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
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
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
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
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
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
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
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
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
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
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
Bayesian optimization for conformer generation
journal, May 2019
- Chan, Lucian; Hutchison, Geoffrey R.; Morris, Garrett M.
- Journal of Cheminformatics, Vol. 11, Issue 1
Bayesian optimization for computationally extensive probability distributions
journal, March 2018
- Tamura, Ryo; Hukushima, Koji
- PLOS ONE, Vol. 13, Issue 3
Finding New Perovskite Halides via Machine Learning
journal, April 2016
- Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho
- Frontiers in Materials, Vol. 3
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
MDTS: automatic complex materials design using Monte Carlo tree search
text, January 2017
- Dieb, Thaer M.; Ju, Shenghong; Yoshizoe, Kazuki
- Taylor & Francis
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
Materials informatics
journal, April 2009
- Rodgers, John
- Materials Science and Technology, Vol. 25, Issue 4
Bayesian Optimization for Conformer Generation
posted_content, October 2018
- Chan, Lucian; Hutchison, Geoffrey; Morris, Garrett
- ChemRxiv
Materials Informatics
journal, April 2009
- Rajan, Krishna; Mendez, Patricio
- Statistical Analysis and Data Mining, Vol. 1, Issue 5
Bayesian Optimization for Conformer Generation
posted_content, February 2019
- Chan, Lucian; Hutchison, Geoffrey; Morris, Garrett
- ChemRxiv
Bayesian Optimization for Conformer Generation
posted_content, October 2018
- Chan, Lucian; Hutchison, Geoffrey; Morris, Garrett
- ChemRxiv
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
Extensive deep neural networks for transferring small scale learning to large scale systems
text, January 2017
- Mills, Kyle; Ryczko, Kevin; Luchak, Iryna
- arXiv
Rapid Bayesian optimisation for synthesis of short polymer fiber materials
preprint, January 2018
- Li, Cheng; Leal, David Rubin de Celis; Rana, Santu
- arXiv
Autonomous Efficient Experiment Design for Materials Discovery with Bayesian Model Averaging
text, January 2018
- Talapatra, Anjana; Boluki, Shahin; Duong, Thien
- arXiv
MDTS: automatic complex materials design using Monte Carlo tree search
text, January 2017
- Dieb, Thaer M.; Ju, Shenghong; Yoshizoe, Kazuki
- Taylor & Francis
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
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
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
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
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
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
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
Finding New Perovskite Halides via Machine Learning
journal, April 2016
- Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho
- Frontiers in Materials, Vol. 3
Bayesian optimization for computationally extensive probability distributions
text, January 2018
- Tamura, Ryo; Hukushima, Koji
- arXiv
MDTS: automatic complex materials design using Monte Carlo tree search
text, January 2017
- Dieb, Thaer M.; Ju, Shenghong; Yoshizoe, Kazuki
- Taylor & Francis