The glassy solid as a statistical ensemble of crystalline microstates
Abstract
We present an alternative and, for the purpose of non-crystalline materials design, a more suitable description of covalent and ionic glassy solids as statistical ensembles of crystalline local minima on the potential energy surface. Motivated by the concept of partially broken ergodicity, we analytically formulate the set of approximations under which the structural features of ergodic systems such as the radial distribution function (RDF) and powder X-ray diffraction (XRD) intensity can be rigorously expressed as statistical ensemble averages over different local minima. Validation is carried out by evaluating these ensemble averages for elemental Si and SiO2 over the local minima obtained through the first-principles random structure sampling that we performed using relatively small simulation cells, thereby restricting the sampling to a set of predominantly crystalline structures. The comparison with XRD and RDF from experiments (amorphous silicon) and molecular dynamics simulations (glassy SiO2) shows excellent agreement, thus supporting the ensemble picture of glasses and opening the door to fully predictive description without the need for experimental inputs.
- Authors:
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); National Institutes of Heath (NIH)
- OSTI Identifier:
- 1618518
- Alternate Identifier(s):
- OSTI ID: 1660018
- Report Number(s):
- NREL/JA-5K00-77246
Journal ID: ISSN 2057-3960; 56; PII: 329
- Grant/Contract Number:
- AC36-08GO28308; T32-GM008759; SC0014664; SC0019306
- Resource Type:
- Published Article
- Journal Name:
- npj Computational Materials
- Additional Journal Information:
- Journal Name: npj Computational Materials Journal Volume: 6 Journal Issue: 1; Journal ID: ISSN 2057-3960
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; atomistic models; structure of solids and liquids
Citation Formats
Jones, Eric B., and Stevanović, Vladan. The glassy solid as a statistical ensemble of crystalline microstates. United Kingdom: N. p., 2020.
Web. doi:10.1038/s41524-020-0329-2.
Jones, Eric B., & Stevanović, Vladan. The glassy solid as a statistical ensemble of crystalline microstates. United Kingdom. https://doi.org/10.1038/s41524-020-0329-2
Jones, Eric B., and Stevanović, Vladan. Thu .
"The glassy solid as a statistical ensemble of crystalline microstates". United Kingdom. https://doi.org/10.1038/s41524-020-0329-2.
@article{osti_1618518,
title = {The glassy solid as a statistical ensemble of crystalline microstates},
author = {Jones, Eric B. and Stevanović, Vladan},
abstractNote = {We present an alternative and, for the purpose of non-crystalline materials design, a more suitable description of covalent and ionic glassy solids as statistical ensembles of crystalline local minima on the potential energy surface. Motivated by the concept of partially broken ergodicity, we analytically formulate the set of approximations under which the structural features of ergodic systems such as the radial distribution function (RDF) and powder X-ray diffraction (XRD) intensity can be rigorously expressed as statistical ensemble averages over different local minima. Validation is carried out by evaluating these ensemble averages for elemental Si and SiO2 over the local minima obtained through the first-principles random structure sampling that we performed using relatively small simulation cells, thereby restricting the sampling to a set of predominantly crystalline structures. The comparison with XRD and RDF from experiments (amorphous silicon) and molecular dynamics simulations (glassy SiO2) shows excellent agreement, thus supporting the ensemble picture of glasses and opening the door to fully predictive description without the need for experimental inputs.},
doi = {10.1038/s41524-020-0329-2},
journal = {npj Computational Materials},
number = 1,
volume = 6,
place = {United Kingdom},
year = {Thu May 14 00:00:00 EDT 2020},
month = {Thu May 14 00:00:00 EDT 2020}
}
https://doi.org/10.1038/s41524-020-0329-2
Web of Science
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