TY - JOUR
T1 - aflow++
T2 - A C++ framework for autonomous materials design
AU - Oses, Corey
AU - Esters, Marco
AU - Hicks, David
AU - Divilov, Simon
AU - Eckert, Hagen
AU - Friedrich, Rico
AU - Mehl, Michael J.
AU - Smolyanyuk, Andriy
AU - Campilongo, Xiomara
AU - van de Walle, Axel
AU - Schroers, Jan
AU - Kusne, A. Gilad
AU - Takeuchi, Ichiro
AU - Zurek, Eva
AU - Nardelli, Marco Buongiorno
AU - Fornari, Marco
AU - Lederer, Yoav
AU - Levy, Ohad
AU - Toher, Cormac
AU - Curtarolo, Stefano
N1 - Funding Information:
After determining the anharmonic IFCs, the Boltzmann Transport Equation is solved to calculate the thermal conductivity tensor. This is a computationally expensive step and is supported by an on-the-fly parallelization scheme inside aflow++ . The calculation conditions are set by the following parameters in the aflow.in file:
Publisher Copyright:
© 2022
PY - 2023/1/25
Y1 - 2023/1/25
N2 - The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets — facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems.
AB - The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets — facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems.
KW - AFLOW
KW - Autonomous computation
KW - Machine learning
KW - Workflows
UR - http://www.scopus.com/inward/record.url?scp=85141920566&partnerID=8YFLogxK
U2 - 10.1016/j.commatsci.2022.111889
DO - 10.1016/j.commatsci.2022.111889
M3 - Article
AN - SCOPUS:85141920566
VL - 217
JO - Computational Materials Science
JF - Computational Materials Science
SN - 0927-0256
M1 - 111889
ER -