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Title: First Principles Prediction of Structure, Structure Selectivity, and Thermodynamic Stability under Realistic Conditions

Abstract

Novel materials are often the enabler for new energy technologies. In ab-initio computational materials science, method are developed to predict the behavior of materials starting from the laws of physics, so that properties can be predicted before compounds have to be synthesized and tested. As such, a virtual materials laboratory can be constructed, saving time and money. The objectives of this program were to develop first-principles theory to predict the structure and thermodynamic stability of materials. Since its inception the program focused on the development of the cluster expansion to deal with the increased complexity of complex oxides. This research led to the incorporation of vibrational degrees of freedom in ab-initio thermodynamics, developed methods for multi-component cluster expansions, included the explicit configurational degrees of freedom of localized electrons, developed the formalism for stability in aqueous environments, and culminated in the first ever approach to produce exact ground state predictions of the cluster expansion. Many of these methods have been disseminated to the larger theory community through the Materials Project, pymatgen software, or individual codes. We summarize three of the main accomplishments.

Authors:
 [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials and Engineering
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division
OSTI Identifier:
1418282
Report Number(s):
DOE-MIT-ER-45571-final
DOE Contract Number:  
FG02-96ER45571
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Ceder, Gerbrand. First Principles Prediction of Structure, Structure Selectivity, and Thermodynamic Stability under Realistic Conditions. United States: N. p., 2018. Web. doi:10.2172/1418282.
Ceder, Gerbrand. First Principles Prediction of Structure, Structure Selectivity, and Thermodynamic Stability under Realistic Conditions. United States. doi:10.2172/1418282.
Ceder, Gerbrand. Sun . "First Principles Prediction of Structure, Structure Selectivity, and Thermodynamic Stability under Realistic Conditions". United States. doi:10.2172/1418282. https://www.osti.gov/servlets/purl/1418282.
@article{osti_1418282,
title = {First Principles Prediction of Structure, Structure Selectivity, and Thermodynamic Stability under Realistic Conditions},
author = {Ceder, Gerbrand},
abstractNote = {Novel materials are often the enabler for new energy technologies. In ab-initio computational materials science, method are developed to predict the behavior of materials starting from the laws of physics, so that properties can be predicted before compounds have to be synthesized and tested. As such, a virtual materials laboratory can be constructed, saving time and money. The objectives of this program were to develop first-principles theory to predict the structure and thermodynamic stability of materials. Since its inception the program focused on the development of the cluster expansion to deal with the increased complexity of complex oxides. This research led to the incorporation of vibrational degrees of freedom in ab-initio thermodynamics, developed methods for multi-component cluster expansions, included the explicit configurational degrees of freedom of localized electrons, developed the formalism for stability in aqueous environments, and culminated in the first ever approach to produce exact ground state predictions of the cluster expansion. Many of these methods have been disseminated to the larger theory community through the Materials Project, pymatgen software, or individual codes. We summarize three of the main accomplishments.},
doi = {10.2172/1418282},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jan 28 00:00:00 EST 2018},
month = {Sun Jan 28 00:00:00 EST 2018}
}

Technical Report:

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