Automating Anharmonic Lattice Thermal Conductivity Calculations using Compressed Sensing Lattice Dynamics [Poster]
Abstract
Engineering heat transfer is critical for applications in heat exchangers, semiconductor devices, thermoelectrics and more. This demand motivates a high-throughput computational methodology for systematically designing materials with improved thermal properties. One emerging method which can rapidly and explicitly consider phonon-phonon interactions even for highly anharmonic materials is Compressive Sensing Lattice Dynamics (CSLD). In fact, CSLD accurately predicts 2nd (harmonic) and 3rd+ order (anharmonic) interatomic force constants (IFCs) with orders of magnitude fewer Density Functional Theory (DFT) calculations than conventional methods. However, doing CSLD can be an exhaustive, many-step process requiring an intimate knowledge of its sensitivity to parameters. Consequently, this work implements an automatic CSLD workflow capable of obtaining the thermal conductivity of potentially thousands of materials, benchmarks the stability of the workflow against materials with a range of anharmonicity, and begins constructing a dataset of thermal conductivity values and phonon dispersion curves to be stored in the Materials Project for public use.
- Authors:
-
- Cornell Univ., Ithaca, NY (United States). Dept. of Materials Science and Engineering
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Technology Area
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22), Materials Sciences & Engineering Division (SC- 22.2)
- OSTI Identifier:
- 1605247
- DOE Contract Number:
- AC02-05CH11231
- Resource Type:
- Conference
- Resource Relation:
- Conference: Summer Intern Program – Presentation of Posters, Berkeley, CA (United States), Summer 2019
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Chang, Rees, Park, Junsoo, Ganose, Alex, and Jain, Anubhav. Automating Anharmonic Lattice Thermal Conductivity Calculations using Compressed Sensing Lattice Dynamics [Poster]. United States: N. p., 2019.
Web.
Chang, Rees, Park, Junsoo, Ganose, Alex, & Jain, Anubhav. Automating Anharmonic Lattice Thermal Conductivity Calculations using Compressed Sensing Lattice Dynamics [Poster]. United States.
Chang, Rees, Park, Junsoo, Ganose, Alex, and Jain, Anubhav. 2019.
"Automating Anharmonic Lattice Thermal Conductivity Calculations using Compressed Sensing Lattice Dynamics [Poster]". United States. https://www.osti.gov/servlets/purl/1605247.
@article{osti_1605247,
title = {Automating Anharmonic Lattice Thermal Conductivity Calculations using Compressed Sensing Lattice Dynamics [Poster]},
author = {Chang, Rees and Park, Junsoo and Ganose, Alex and Jain, Anubhav},
abstractNote = {Engineering heat transfer is critical for applications in heat exchangers, semiconductor devices, thermoelectrics and more. This demand motivates a high-throughput computational methodology for systematically designing materials with improved thermal properties. One emerging method which can rapidly and explicitly consider phonon-phonon interactions even for highly anharmonic materials is Compressive Sensing Lattice Dynamics (CSLD). In fact, CSLD accurately predicts 2nd (harmonic) and 3rd+ order (anharmonic) interatomic force constants (IFCs) with orders of magnitude fewer Density Functional Theory (DFT) calculations than conventional methods. However, doing CSLD can be an exhaustive, many-step process requiring an intimate knowledge of its sensitivity to parameters. Consequently, this work implements an automatic CSLD workflow capable of obtaining the thermal conductivity of potentially thousands of materials, benchmarks the stability of the workflow against materials with a range of anharmonicity, and begins constructing a dataset of thermal conductivity values and phonon dispersion curves to be stored in the Materials Project for public use.},
doi = {},
url = {https://www.osti.gov/biblio/1605247},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Aug 07 00:00:00 EDT 2019},
month = {Wed Aug 07 00:00:00 EDT 2019}
}