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

Abstract

Modern ab initio methods have rapidly increased our understanding of solid state materials properties, chemical reactions, and the quantum interactions between atoms. However, poor scaling often renders direct ab initio calculations intractable for large or complex systems. There are two obvious avenues through which to remedy this problem: (i) develop new, less expensive methods to calculate system properties, or (ii) make existing methods faster. This paper describes an open source framework designed to pursue both of these avenues. PROPhet (short for PROPerty Prophet) utilizes machine learning techniques to find complex, non-linear mappings between sets of material or system properties. The result is a single code capable of learning analytical potentials, non-linear density functionals, and other structure-property or property-property relationships. These capabilities enable highly accurate mesoscopic simulations, facilitate computation of expensive properties, and enable the development of predictive models for systematic materials design and optimization. Here, this work explores the coupling of machine learning to ab initio methods through means both familiar (e.g., the creation of various potentials and energy functionals) and less familiar (e.g., the creation of density functionals for arbitrary properties), serving both to demonstrate PROPhet’s ability to create exciting post-processing analysis tools and to open the door tomore » improving ab initio methods themselves with these powerful machine learning techniques.« less

Authors:
 [1];  [2];  [2]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Mechanical Engineering; Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Chemistry and Chemical Biology
  2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Mechanical Engineering
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Alliance for Sustainable Energy, LLC, Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1368375
Grant/Contract Number:  
SC0001299; FG02-09ER46577; AC36-8GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 7; Journal Issue: 1; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

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. https://www.osti.gov/servlets/purl/1368375.
@article{osti_1368375,
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 = {Modern ab initio methods have rapidly increased our understanding of solid state materials properties, chemical reactions, and the quantum interactions between atoms. However, poor scaling often renders direct ab initio calculations intractable for large or complex systems. There are two obvious avenues through which to remedy this problem: (i) develop new, less expensive methods to calculate system properties, or (ii) make existing methods faster. This paper describes an open source framework designed to pursue both of these avenues. PROPhet (short for PROPerty Prophet) utilizes machine learning techniques to find complex, non-linear mappings between sets of material or system properties. The result is a single code capable of learning analytical potentials, non-linear density functionals, and other structure-property or property-property relationships. These capabilities enable highly accurate mesoscopic simulations, facilitate computation of expensive properties, and enable the development of predictive models for systematic materials design and optimization. Here, this work explores the coupling of machine learning to ab initio methods through means both familiar (e.g., the creation of various potentials and energy functionals) and less familiar (e.g., the creation of density functionals for arbitrary properties), serving both to demonstrate PROPhet’s ability to create exciting post-processing analysis tools and to open the door to improving ab initio methods themselves with these powerful machine learning techniques.},
doi = {10.1038/s41598-017-01251-z},
journal = {Scientific Reports},
number = 1,
volume = 7,
place = {United States},
year = {2017},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
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Cited by: 3 works
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Figures / Tables:

Figure 1 Figure 1: Histogram of the errors between a machine-learned analytical potential and the directly computed DFT energies for carbon in the diamond structure. The network used here, trained in PROPhet, contained 2 hidden layers each containing 35 nodes. The errors shown here are for 2000 structures that were not usedmore » in the fitting procedure.« less

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    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.