Density functional theory calculations of generalized stacking fault energy surfaces for eight face-centered cubic transition metals

2019 ◽  
Vol 126 (10) ◽  
pp. 105112 ◽  
Author(s):  
Yanqing Su ◽  
Shuozhi Xu ◽  
Irene J. Beyerlein
Keyword(s):  
Density Functional Theory ◽  
Transition Metals ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Face Centered Cubic ◽  
Functional Theory ◽  
Face Centered ◽  
Generalized Stacking Fault ◽  
Energy Surfaces ◽  
Generalized Stacking Fault Energy

scholarly journals The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

2020 ◽  
Author(s):  
Justin S. Smith ◽  
Roman Zubatyuk ◽  
Benjamin T. Nebgen ◽  
Nicholas Lubbers ◽  
Kipton Barros ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Potential Energy Surfaces ◽  
Density Functional ◽  
Atomic Charge ◽  
Density Functional Theory Calculations ◽  
Coupled Cluster ◽  
Data Sets ◽  
Data Set ◽  
Functional Theory ◽  
Energy Surfaces

<p>Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based eneral-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data set diversity, we visualize them with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5M density functional theory calculations, while the ANI-1ccx data set contains 500k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM properties from density functional theory and coupled cluster are provided: energies, atomic forces, multipole moments, atomic charges, and more. We provide this data to the community to aid research and development of ML models for chemistry.</p>

Symmetry reduction of δ-plutonium: an electronic-structure effect

2005 ◽  
Vol 893 ◽  
Author(s):  
Kevin T. Moore ◽  
Per Söderlind ◽  
Adam J. Schwartz ◽  
David Laughlin
Keyword(s):  
Electronic Structure ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Monoclinic Space Group ◽  
Face Centered Cubic ◽  
Functional Theory ◽  
Space Group ◽  
Bond Strengths ◽  
Reduced Space ◽  
Face Centered

AbstractUsing first-principles density-functional theory calculations, we show that the anomalously large anisotropy of δ-plutonium is a consequence of greatly varying bond-strengths between the 12 nearest neighbors. Employing the calculated bond strengths, we expand the tenants of classical crystallography by incorporating anisotropy of chemical bonds, which yields a structure with the monoclinic space group Cm for δ-plutonium rather than face-centered cubic Fm3m. The reduced space group for δ-plutonium enlightens why the ground state of the metal is monoclinic, why distortions of the metal are viable, and has considerable implications for the behavior of the material as it ages. These results illustrate how an expansion of classical crystallography that accounts for anisotropic electronic structure can explain complicated materials in a novel way.

scholarly journals The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

2020 ◽  
Author(s):  
Justin S. Smith ◽  
Roman Zubatyuk ◽  
Benjamin T. Nebgen ◽  
Nicholas Lubbers ◽  
Kipton Barros ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Potential Energy Surfaces ◽  
Density Functional ◽  
Atomic Charge ◽  
Density Functional Theory Calculations ◽  
Coupled Cluster ◽  
Data Sets ◽  
Data Set ◽  
Functional Theory ◽  
Energy Surfaces

<p>Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based eneral-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data set diversity, we visualize them with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5M density functional theory calculations, while the ANI-1ccx data set contains 500k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM properties from density functional theory and coupled cluster are provided: energies, atomic forces, multipole moments, atomic charges, and more. We provide this data to the community to aid research and development of ML models for chemistry.</p>

Sign in / Sign up

Export Citation Format

Share Document