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

Title: Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods

Authors:
; ;
Publication Date:
Research Org.:
Energy Frontier Research Centers (EFRC) (United States). Center for Next Generation of Materials by Design: Incorporating Metastability (CNGMD)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1388993
DOE Contract Number:  
AC36-99GO10337
Resource Type:
Journal Article
Resource Relation:
Journal Name: Scientific Reports; Journal Volume: 7; Journal Issue: 1; Related Information: CNGMD partners with National Renewable Energy Laboratory (lead); Colorado School of Mines; Harvard University; Lawrence Berkeley National Laboratory; Massachusetts Institute of Technology; Oregon State University; SLAC National Accelerator Laboratory
Country of Publication:
United States
Language:
English
Subject:
solar (photovoltaic), solar (fuels), solid state lighting, phonons, thermoelectric, hydrogen and fuel cells, defects, charge transport, optics, materials and chemistry by design, synthesis (novel materials)

Citation Formats

Kolb, Brian, Lentz, Levi C., and Kolpak, Alexie M.. Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods. United States: N. p., 2017. Web. doi:10.1038/s41598-017-01251-z.
Kolb, Brian, Lentz, Levi C., & Kolpak, Alexie M.. Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods. United States. doi:10.1038/s41598-017-01251-z.
Kolb, Brian, Lentz, Levi C., and Kolpak, Alexie M.. Wed . "Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods". United States. doi:10.1038/s41598-017-01251-z.
@article{osti_1388993,
title = {Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods},
author = {Kolb, Brian and Lentz, Levi C. and Kolpak, Alexie M.},
abstractNote = {},
doi = {10.1038/s41598-017-01251-z},
journal = {Scientific Reports},
number = 1,
volume = 7,
place = {United States},
year = {Wed Apr 26 00:00:00 EDT 2017},
month = {Wed Apr 26 00:00:00 EDT 2017}
}