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Title: Ocean of Data: Integrating First-Principles Calculations and CALPHAD Modeling with Machine Learning

Abstract

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable or unstable, when interacting with the surroundings. Computational thermodynamics enables quantitative calculations of thermodynamic properties as a function of both external conditions and internal configurations, in terms of first and second derivatives of energy with respect to either potentials or molar quantities. Thermodynamic modeling based on the CALPHAD method enables the thermodynamics beyond stable states and is the foundation of Materials Genome and materials design. In last several decades, first-principles calculations based on density functional theory have provided invaluable thermochemical data to improve the robustness of CALPHAD modeling. Today with ever increasing computing power and large amount of data repositories online, it calls for a new paradigm for CALPHAD modeling approach incorporating machine learning to create a sustainable ecosystem for the ocean of data and for emergent behaviors.

Authors:
 [1]
  1. Pennsylvania State Univ., University Park, PA (United States). Dept. of Materials Science and Engineering
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1543497
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Journal of Phase Equilibria and Diffusion
Additional Journal Information:
Journal Volume: 39; Journal Issue: 5; Journal ID: ISSN 1547-7037
Publisher:
ASM International
Country of Publication:
United States
Language:
English
Subject:
Chemistry; Materials Science; Metallurgy & Metallurgical Engineering

Citation Formats

Liu, Zi-Kui. Ocean of Data: Integrating First-Principles Calculations and CALPHAD Modeling with Machine Learning. United States: N. p., 2018. Web. doi:10.1007/s11669-018-0654-z.
Liu, Zi-Kui. Ocean of Data: Integrating First-Principles Calculations and CALPHAD Modeling with Machine Learning. United States. doi:10.1007/s11669-018-0654-z.
Liu, Zi-Kui. Mon . "Ocean of Data: Integrating First-Principles Calculations and CALPHAD Modeling with Machine Learning". United States. doi:10.1007/s11669-018-0654-z.
@article{osti_1543497,
title = {Ocean of Data: Integrating First-Principles Calculations and CALPHAD Modeling with Machine Learning},
author = {Liu, Zi-Kui},
abstractNote = {Thermodynamics is a science concerning the state of a system, whether it is stable, metastable or unstable, when interacting with the surroundings. Computational thermodynamics enables quantitative calculations of thermodynamic properties as a function of both external conditions and internal configurations, in terms of first and second derivatives of energy with respect to either potentials or molar quantities. Thermodynamic modeling based on the CALPHAD method enables the thermodynamics beyond stable states and is the foundation of Materials Genome and materials design. In last several decades, first-principles calculations based on density functional theory have provided invaluable thermochemical data to improve the robustness of CALPHAD modeling. Today with ever increasing computing power and large amount of data repositories online, it calls for a new paradigm for CALPHAD modeling approach incorporating machine learning to create a sustainable ecosystem for the ocean of data and for emergent behaviors.},
doi = {10.1007/s11669-018-0654-z},
journal = {Journal of Phase Equilibria and Diffusion},
issn = {1547-7037},
number = 5,
volume = 39,
place = {United States},
year = {2018},
month = {7}
}

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