Eric Gossett
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View article: The AFLOW Library of Crystallographic Prototypes: Part 4
The AFLOW Library of Crystallographic Prototypes: Part 4 Open
The AFLOW Library of Crystallographic Prototypes has been updated to include an additional 683 entries, which now reaches 1,783 prototypes. We have also made some changes to the presentation of the entries, including a more consistent defi…
View article: Universal fragment descriptors for predicting properties of inorganic crystals
Universal fragment descriptors for predicting properties of inorganic crystals Open
Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, d…
View article: Predicting superhard materials via a machine learning informed evolutionary structure search
Predicting superhard materials via a machine learning informed evolutionary structure search Open
The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications. Herein, good agreement was found between experimental Vickers hardnesses…
View article: The AFLOW Library of Crystallographic Prototypes: Part 2
The AFLOW Library of Crystallographic Prototypes: Part 2 Open
Materials discovery via high-throughput methods relies on the availability of structural prototypes, which are generally decorated with varying combinations of elements to produce potential new materials. To facilitate the automatic genera…
View article: AFLOW-CHULL: Cloud-oriented platform for autonomous phase stability\n analysis
AFLOW-CHULL: Cloud-oriented platform for autonomous phase stability\n analysis Open
$\\textit{A priori}$ prediction of phase stability of materials is a\nchallenging practice, requiring knowledge of all energetically-competing\nstructures at formation conditions. Large materials repositories\n$\\unicode{x2014}$ housing pr…
View article: AFLOW-CHULL: Cloud-oriented platform for autonomous phase stability analysis
AFLOW-CHULL: Cloud-oriented platform for autonomous phase stability analysis Open
$\textit{A priori}$ prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically-competing structures at formation conditions. Large materials repositories $\unicode{x2014}$ housing propert…
View article: AFLOW-SYM: Platform for the complete, automatic and self-consistent symmetry analysis of crystals
AFLOW-SYM: Platform for the complete, automatic and self-consistent symmetry analysis of crystals Open
Determination of the symmetry profile of structures is a persistent challenge in materials science. Results often vary amongst standard packages, hindering autonomous materials development by requiring continuous user attention and educate…
View article: The AFLOW Fleet for Materials Discovery
The AFLOW Fleet for Materials Discovery Open
The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computat…
View article: AFLOW-ML: A RESTful API for machine-learning predictions of materials\n properties
AFLOW-ML: A RESTful API for machine-learning predictions of materials\n properties Open
Machine learning approaches, enabled by the emergence of comprehensive\ndatabases of materials properties, are becoming a fruitful direction for\nmaterials analysis. As a result, a plethora of models have been constructed and\ntrained on e…
View article: AFLUX: The LUX materials search API for the AFLOW data repositories
AFLUX: The LUX materials search API for the AFLOW data repositories Open
Automated computational materials science frameworks rapidly generate large quantities of materials data useful for accelerated materials design. We have extended the data oriented AFLOW-repository API (Application-Program-Interface, as de…