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Title: Physics-informed machine learning for inorganic scintillator discovery

Abstract

Applications of inorganic scintillators—activated with lanthanide dopants, such as Ce and Eu—are found in diverse fields. As a strict requirement to exhibit scintillation, the 4f ground state (with the electronic configuration of [Xe]4fn 5d0) and 5d1 lowest excited state (with the electronic configuration of [Xe]4fn–1 5d1) levels induced by the activator must lie within the host bandgap. Here we introduce a new machine learning (ML) based search strategy for high-throughput chemical space explorations to discover and design novel inorganic scintillators. Building upon well-known physics-based chemical trends for the host dependent electron binding energies within the 4f and 5d1 energy levels of lanthanide ions and available experimental data, the developed ML model—coupled with knowledge of the vacuum referred valence and conduction band edges computed from first principles—can rapidly and reliably estimate the relative positions of the activator’s energy levels relative to the valence and conduction band edges of any given host chemistry.

Authors:
ORCiD logo [1]; 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 Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1467213
Alternate Identifier(s):
OSTI ID: 1434562
Report Number(s):
LA-UR-18-21016
Journal ID: ISSN 0021-9606
Grant/Contract Number:  
AC52-06NA25396; 20180009DR
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 148; Journal Issue: 24; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Pilania, Ghanshyam, McClellan, Kenneth James, Stanek, Christopher Richard, and Uberuaga, Blas P. Physics-informed machine learning for inorganic scintillator discovery. United States: N. p., 2018. Web. doi:10.1063/1.5025819.
Pilania, Ghanshyam, McClellan, Kenneth James, Stanek, Christopher Richard, & Uberuaga, Blas P. Physics-informed machine learning for inorganic scintillator discovery. United States. doi:10.1063/1.5025819.
Pilania, Ghanshyam, McClellan, Kenneth James, Stanek, Christopher Richard, and Uberuaga, Blas P. Wed . "Physics-informed machine learning for inorganic scintillator discovery". United States. doi:10.1063/1.5025819. https://www.osti.gov/servlets/purl/1467213.
@article{osti_1467213,
title = {Physics-informed machine learning for inorganic scintillator discovery},
author = {Pilania, Ghanshyam and McClellan, Kenneth James and Stanek, Christopher Richard and Uberuaga, Blas P.},
abstractNote = {Applications of inorganic scintillators—activated with lanthanide dopants, such as Ce and Eu—are found in diverse fields. As a strict requirement to exhibit scintillation, the 4f ground state (with the electronic configuration of [Xe]4fn 5d0) and 5d1 lowest excited state (with the electronic configuration of [Xe]4fn–1 5d1) levels induced by the activator must lie within the host bandgap. Here we introduce a new machine learning (ML) based search strategy for high-throughput chemical space explorations to discover and design novel inorganic scintillators. Building upon well-known physics-based chemical trends for the host dependent electron binding energies within the 4f and 5d1 energy levels of lanthanide ions and available experimental data, the developed ML model—coupled with knowledge of the vacuum referred valence and conduction band edges computed from first principles—can rapidly and reliably estimate the relative positions of the activator’s energy levels relative to the valence and conduction band edges of any given host chemistry.},
doi = {10.1063/1.5025819},
journal = {Journal of Chemical Physics},
number = 24,
volume = 148,
place = {United States},
year = {2018},
month = {4}
}

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