skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators

Abstract

The use of machine learning (ML) models toward development of validated structure–property relationships for two fundamental properties of activated inorganic scintillators for high energy radiation detection, namely the light yield (LY) and the decay time constant, is explored. The ML models are built on easily accessible proxies of materials—interchangeably referred to as features, descriptors or fingerprints—that are carefully selected on the basis of a physical understanding of the scintillation mechanism. Our study indicates that the developed physics-based ML models employing kernel ridge regression (KRR) and AdaBoost algorithm applied on top of a decision tree-based regression are able to “learn” the underlying design rules in a multi-dimensional feature space and thereby enable reasonably accurate predictions of the two target properties on unseen compounds (i.e., on a held-out test set). For instance, within a set of twenty-five cerium- or europium-doped scintillator materials, our analysis reveals a strong correlation between the average ionic part of the dielectric constant and the LY, irrespective of the specific chemistry of the compounds, indicating that the average ionic part of the dielectric constant is a particularly relevant descriptor toward prediction of the LY. Our results also demonstrate that, despite the use of small training datasets, the developedmore » models are able to quickly distinguish high performing chemistries from those with relatively poor performance and therefore can play a crucial role in screening of new compounds with an attractive combination of targeted properties. Finally, the present study provides necessary motivation for future efforts involving ML models with relatively large training datasets, vast feature space explorations, and experimental design in search of promising novel scintillator chemistries.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1511616
Report Number(s):
LA-UR-18-29903
Journal ID: ISSN 0022-2461
Grant/Contract Number:  
89233218CNA000001; AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Materials Science
Additional Journal Information:
Journal Volume: 54; Journal Issue: 11; Journal ID: ISSN 0022-2461
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Pilania, Ghanshyam, Liu, Xiang-Yang, and Wang, Zhehui. Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators. United States: N. p., 2019. Web. doi:10.1007/s10853-019-03434-7.
Pilania, Ghanshyam, Liu, Xiang-Yang, & Wang, Zhehui. Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators. United States. doi:10.1007/s10853-019-03434-7.
Pilania, Ghanshyam, Liu, Xiang-Yang, and Wang, Zhehui. Tue . "Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators". United States. doi:10.1007/s10853-019-03434-7. https://www.osti.gov/servlets/purl/1511616.
@article{osti_1511616,
title = {Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators},
author = {Pilania, Ghanshyam and Liu, Xiang-Yang and Wang, Zhehui},
abstractNote = {The use of machine learning (ML) models toward development of validated structure–property relationships for two fundamental properties of activated inorganic scintillators for high energy radiation detection, namely the light yield (LY) and the decay time constant, is explored. The ML models are built on easily accessible proxies of materials—interchangeably referred to as features, descriptors or fingerprints—that are carefully selected on the basis of a physical understanding of the scintillation mechanism. Our study indicates that the developed physics-based ML models employing kernel ridge regression (KRR) and AdaBoost algorithm applied on top of a decision tree-based regression are able to “learn” the underlying design rules in a multi-dimensional feature space and thereby enable reasonably accurate predictions of the two target properties on unseen compounds (i.e., on a held-out test set). For instance, within a set of twenty-five cerium- or europium-doped scintillator materials, our analysis reveals a strong correlation between the average ionic part of the dielectric constant and the LY, irrespective of the specific chemistry of the compounds, indicating that the average ionic part of the dielectric constant is a particularly relevant descriptor toward prediction of the LY. Our results also demonstrate that, despite the use of small training datasets, the developed models are able to quickly distinguish high performing chemistries from those with relatively poor performance and therefore can play a crucial role in screening of new compounds with an attractive combination of targeted properties. Finally, the present study provides necessary motivation for future efforts involving ML models with relatively large training datasets, vast feature space explorations, and experimental design in search of promising novel scintillator chemistries.},
doi = {10.1007/s10853-019-03434-7},
journal = {Journal of Materials Science},
number = 11,
volume = 54,
place = {United States},
year = {2019},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 2 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
journal, April 2016

  • Agrawal, Ankit; Choudhary, Alok
  • APL Materials, Vol. 4, Issue 5
  • DOI: 10.1063/1.4946894

From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
journal, February 2016


Generalized Gradient Approximation Made Simple
journal, October 1996

  • Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
  • Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868
  • DOI: 10.1103/PhysRevLett.77.3865

Predictions of new AB O 3 perovskite compounds by combining machine learning and density functional theory
journal, April 2018


Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation
journal, December 2015

  • Hong, Wesley T.; Welsch, Roy E.; Shao-Horn, Yang
  • The Journal of Physical Chemistry C, Vol. 120, Issue 1
  • DOI: 10.1021/acs.jpcc.5b10071

High-Throughput Combinatorial Database of Electronic Band Structures for Inorganic Scintillator Materials
journal, June 2011

  • Setyawan, Wahyu; Gaume, Romain M.; Lam, Stephanie
  • ACS Combinatorial Science, Vol. 13, Issue 4
  • DOI: 10.1021/co200012w

Projector augmented-wave method
journal, December 1994


New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships
journal, April 2016

  • Jain, Anubhav; Hautier, Geoffroy; Ong, Shyue Ping
  • Journal of Materials Research, Vol. 31, Issue 8
  • DOI: 10.1557/jmr.2016.80

On Predicting the Maximum Efficiency of Phosphor Systems Excited by Ionizing Radiation
journal, January 1980

  • Robbins, D. J.
  • Journal of The Electrochemical Society, Vol. 127, Issue 12
  • DOI: 10.1149/1.2129574

Efficient exploration of the High Entropy Alloy composition-phase space
journal, June 2018


The quest for the ideal inorganic scintillator
journal, June 2003

  • Derenzo, S. E.; Weber, M. J.; Bourret-Courchesne, E.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 505, Issue 1-2, p. 111-117
  • DOI: 10.1016/S0168-9002(03)01031-3

Stoichiometric cerium compounds as scintillators, II. CeP/sub 5/O/sub 14/
journal, January 1992

  • Wojtowicz, A. J.; Berman, E.; Lempicki, A.
  • IEEE Transactions on Nuclear Science, Vol. 39, Issue 5
  • DOI: 10.1109/23.173240

Scintillation detectors for x-rays
journal, February 2006


Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach
journal, September 2017

  • Furmanchuk, Al'ona; Saal, James E.; Doak, Jeff W.
  • Journal of Computational Chemistry, Vol. 39, Issue 4
  • DOI: 10.1002/jcc.25067

New single crystal scintillators: CsCaCl3:Eu and CsCaI3:Eu
journal, August 2012


NOMAD: The FAIR concept for big data-driven materials science
journal, September 2018


Finding optimal surface sites on heterogeneous catalysts by counting nearest neighbors
journal, October 2015


From ultrasoft pseudopotentials to the projector augmented-wave method
journal, January 1999


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

  • Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam
  • npj Computational Materials, Vol. 3, Issue 1
  • DOI: 10.1038/s41524-017-0056-5

SchNet – A deep learning architecture for molecules and materials
journal, June 2018

  • Schütt, K. T.; Sauceda, H. E.; Kindermans, P. -J.
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5019779

Machine learning for quantum mechanics in a nutshell
journal, July 2015

  • Rupp, Matthias
  • International Journal of Quantum Chemistry, Vol. 115, Issue 16
  • DOI: 10.1002/qua.24954

Machine learning properties of binary wurtzite superlattices
journal, January 2018


Uncovering structure-property relationships of materials by subgroup discovery
journal, January 2017

  • Goldsmith, Bryan R.; Boley, Mario; Vreeken, Jilles
  • New Journal of Physics, Vol. 19, Issue 1
  • DOI: 10.1088/1367-2630/aa57c2

Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX 3 Perovskites
journal, June 2016

  • Kim, Chiho; Pilania, Ghanshyam; Ramprasad, Rampi
  • The Journal of Physical Chemistry C, Vol. 120, Issue 27
  • DOI: 10.1021/acs.jpcc.6b05068

Critical assessment of regression-based machine learning methods for polymer dielectrics
journal, December 2016


Physics-informed machine learning for inorganic scintillator discovery
journal, June 2018

  • Pilania, G.; McClellan, K. J.; Stanek, C. R.
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5025819

Rational design of binary halide scintillators via data mining
journal, July 2012

  • Kong, Chang Sun; Rajan, Krishna
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 680
  • DOI: 10.1016/j.nima.2012.03.050

Hierarchical modeling of molecular energies using a deep neural network
journal, June 2018

  • Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5011181

Fundamental limits of scintillator performance
journal, September 1993

  • Lempicki, A.; Wojtowicz, A. J.; Berman, E.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 333, Issue 2-3
  • DOI: 10.1016/0168-9002(93)91170-R

Design and Implementation of a Facility for Discovering New Scintillator Materials
journal, June 2008

  • Derenzo, Stephen E.; Boswell, Martin S.; Bourret-Courchesne, Edith
  • IEEE Transactions on Nuclear Science, Vol. 55, Issue 3
  • DOI: 10.1109/TNS.2008.921932

Discovering a Transferable Charge Assignment Model Using Machine Learning
journal, July 2018

  • Sifain, Andrew E.; Lubbers, Nicholas; Nebgen, Benjamin T.
  • The Journal of Physical Chemistry Letters, Vol. 9, Issue 16
  • DOI: 10.1021/acs.jpclett.8b01939

A universal strategy for the creation of machine learning-based atomistic force fields
journal, September 2017


Crystal growth and characterization of europium doped KCaI3, a high light yield scintillator
journal, October 2015


Accelerating materials property predictions using machine learning
journal, September 2013

  • Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun
  • Scientific Reports, Vol. 3, Issue 1
  • DOI: 10.1038/srep02810

Alchemical and structural distribution based representation for universal quantum machine learning
journal, June 2018

  • Faber, Felix A.; Christensen, Anders S.; Huang, Bing
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5020710

Inverse molecular design using machine learning: Generative models for matter engineering
journal, July 2018


Accelerated search for materials with targeted properties by adaptive design
journal, April 2016

  • Xue, Dezhen; Balachandran, Prasanna V.; Hogden, John
  • Nature Communications, Vol. 7, Issue 1
  • DOI: 10.1038/ncomms11241

Estimation of the Electron Thermalization Length in Ionic Materials
journal, September 2013

  • Belsky, Andrei; Ivanovskikh, Konstantin; Vasil’ev, Andrey
  • The Journal of Physical Chemistry Letters, Vol. 4, Issue 20
  • DOI: 10.1021/jz401864w

Accelerating the discovery of materials for clean energy in the era of smart automation
journal, April 2018

  • Tabor, Daniel P.; Roch, Loïc M.; Saikin, Semion K.
  • Nature Reviews Materials, Vol. 3, Issue 5
  • DOI: 10.1038/s41578-018-0005-z

The physics of inorganic scintillators
journal, July 1995


Phonons and related crystal properties from density-functional perturbation theory
journal, July 2001

  • Baroni, Stefano; de Gironcoli, Stefano; Dal Corso, Andrea
  • Reviews of Modern Physics, Vol. 73, Issue 2
  • DOI: 10.1103/RevModPhys.73.515

Scintillation Properties of Eu$^{2+}$-Activated Barium Fluoroiodide
journal, June 2010

  • Gundiah, Gautam; Bourret-Courchesne, Edith; Bizarri, Gregory
  • IEEE Transactions on Nuclear Science, Vol. 57, Issue 3
  • DOI: 10.1109/TNS.2010.2045513

Machine learning bandgaps of double perovskites
journal, January 2016

  • Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.
  • Scientific Reports, Vol. 6, Issue 1
  • DOI: 10.1038/srep19375

A Formation Energy Predictor for Crystalline Materials Using Ensemble Data Mining
conference, December 2016

  • Agrawal, Ankit; Meredig, Bryce; Wolverton, Chris
  • 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)
  • DOI: 10.1109/ICDMW.2016.0183

Special points for Brillouin-zone integrations
journal, June 1976

  • Monkhorst, Hendrik J.; Pack, James D.
  • Physical Review B, Vol. 13, Issue 12, p. 5188-5192
  • DOI: 10.1103/PhysRevB.13.5188

Lu2S3:Ce3+, A new red luminescing scintillator
journal, February 1998

  • van't Spijker, J. C.; Dorenbos, P.; Allier, C. P.
  • Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, Vol. 134, Issue 2
  • DOI: 10.1016/S0168-583X(98)00667-3

Light output and energy resolution of Ce3+-doped scintillators
journal, June 2002

  • Dorenbos, Pieter
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 486, Issue 1-2
  • DOI: 10.1016/S0168-9002(02)00704-0

Data-driven learning and prediction of inorganic crystal structures
journal, January 2018

  • Deringer, Volker L.; Proserpio, Davide M.; Csányi, Gábor
  • Faraday Discussions, Vol. 211
  • DOI: 10.1039/C8FD00034D

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


VESTA : a three-dimensional visualization system for electronic and structural analysis
journal, May 2008


Classification of AB O 3 perovskite solids: a machine learning study
journal, September 2015

  • Pilania, G.; Balachandran, P. V.; Gubernatis, J. E.
  • Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials, Vol. 71, Issue 5
  • DOI: 10.1107/S2052520615013979

Atomic Radii in Crystals
journal, November 1964

  • Slater, J. C.
  • The Journal of Chemical Physics, Vol. 41, Issue 10
  • DOI: 10.1063/1.1725697

Energy Loss in Inorganic Scintillators
journal, January 1995

  • Rodnyi, P. A.; Dorenbos, P.; van Eijk, C. W. E.
  • physica status solidi (b), Vol. 187, Issue 1
  • DOI: 10.1002/pssb.2221870102

Finding New Perovskite Halides via Machine Learning
journal, April 2016

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

Big Data, new epistemologies and paradigm shifts
journal, April 2014


Mixed Lutetium Iodide Compounds
journal, June 2008

  • Glodo, Jarek; van Loef, Edgar V. D.; Higgins, William M.
  • IEEE Transactions on Nuclear Science, Vol. 55, Issue 3
  • DOI: 10.1109/TNS.2008.922215

FINDSYM : program for identifying the space-group symmetry of a crystal
journal, January 2005


Growth and characterization of potassium strontium iodide: A new high light yield scintillator with 2.4% energy resolution
journal, April 2015

  • Stand, L.; Zhuravleva, M.; Lindsey, A.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 780
  • DOI: 10.1016/j.nima.2015.01.052

Informatics derived materials databases for multifunctional properties
journal, February 2015


Eu2+-activated BaCl2, BaBr2 and BaI2 scintillators revisited
journal, January 2014

  • Yan, Zewu; Gundiah, Gautam; Bizarri, Gregory A.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 735
  • DOI: 10.1016/j.nima.2013.09.021

Big Data of Materials Science: Critical Role of the Descriptor
journal, March 2015


Structure and scintillation of Eu2+-activated BaBrCl and solid solutions in the BaCl2–BaBr2 system
journal, June 2013


Benchmarking density functional perturbation theory to enable high-throughput screening of materials for dielectric constant and refractive index
journal, March 2016


Machine Learning Strategy for Accelerated Design of Polymer Dielectrics
journal, February 2016

  • Mannodi-Kanakkithodi, Arun; Pilania, Ghanshyam; Huan, Tran Doan
  • Scientific Reports, Vol. 6, Issue 1
  • DOI: 10.1038/srep20952

Scintillation and Optical Properties of ${\rm BaBrI}\!:\!{\rm Eu}^{2+}$ and ${\rm CsBa}_{2}{\rm I}_{5}\!:\!{\rm Eu}^{2+}$
journal, December 2011

  • Bizarri, Gregory; Bourret-Courchesne, Edith D.; Yan, Zewu
  • IEEE Transactions on Nuclear Science, Vol. 58, Issue 6
  • DOI: 10.1109/TNS.2011.2166999

Feature engineering of machine-learning chemisorption models for catalyst design
journal, February 2017


Scintillation properties of Eu2+-doped KBa2I5 and K2BaI4
journal, January 2016


Predicting materials properties and behavior using classification and regression trees
journal, October 2006


Scintillation properties of CsBa2Br5:Eu2+
journal, October 2011

  • Borade, Ramesh; Bourret-Courchesne, Edith; Derenzo, Stephen
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 652, Issue 1
  • DOI: 10.1016/j.nima.2010.08.093

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


Machine learning based interatomic potential for amorphous carbon
journal, March 2017


Machine learning for molecular and materials science
journal, July 2018


Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores
journal, November 2016


Crystal growth and scintillation properties of potassium strontium bromide
journal, August 2015


Structure maps for. Pseudobinary and ternary phases
journal, August 1988


Optical and scintillation properties of CsBa2I5:Eu2+
journal, January 2014


Informatics-Based Uncertainty Quantification in the Design of Inorganic Scintillators
journal, July 2013

  • Ganguly, Subhas; Kong, Chang Sun; Broderick, Scott R.
  • Materials and Manufacturing Processes, Vol. 28, Issue 7
  • DOI: 10.1080/10426914.2012.736660