skip to main content

DOE PAGESDOE PAGES

Title: Efficient first-principles prediction of solid stability: Towards chemical accuracy

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. In this paper, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for main group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.
Authors:
 [1] ; ORCiD logo [2] ;  [3] ;  [3] ;  [2] ;  [4] ;  [5] ;  [6] ;  [7] ;  [8] ; ORCiD logo [1]
  1. Tulane Univ., New Orleans, LA (United States). Dept. of Physics and Engineering Physics
  2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials Science and Engineering
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division; Univ. of California, Berkeley, CA (United States). Dept. of Materials Science and Engineering
  4. Univ. of Basel (Switzerland). Inst. of Physical Chemistry, National Center for Computational Design and Discovery of Novel Materials and Dept. of Chemistry
  5. Martin Luther Univ. of Halle-Wittenberg, Halle (Germany). Inst. of Physics
  6. Temple Univ., Philadelphia, PA (United States). Dept. of Physics
  7. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials Science and Engineering; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division; Univ. of California, Berkeley, CA (United States). Dept. of Materials Science and Engineering
  8. Temple Univ., Philadelphia, PA (United States). Dept. of Physics and Dept. of Chemistry
Publication Date:
Grant/Contract Number:
AC02-05CH11231; SC0012575; MA-6786/6
Type:
Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 4; Journal Issue: 1; Related Information: © 2018 The Author(s).; Journal ID: ISSN 2057-3960
Publisher:
Nature Publishing Group
Research Org:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); National Renewable Energy Lab. (NREL), 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); USDOD; German Research Foundation (DFG)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; electronic structure; materials chemistry
OSTI Identifier:
1434035