10 citations · 10 across the 4 of their papers we have counts for
5 papers
Predicting Superhard Materials via a Machine Learning Informed Evolutionary Structure Search
Patrick Avery, Xiaoyu Wang, Davide M. Proserpio +5
Good agreement was found between experimental Vickers hardnesses, , of a wide range of materials and those calculated by three macroscopic hardness models that employ t…
Unavoidable disorder and entropy in multi-component systems
Cormac Toher, Corey Oses, David Hicks +1
The need for improved functionalities is driving the search for more complicated multi-component materials. Despite the factorially increasing composition space, ordered compounds…
The AFLOW Fleet for Materials Discovery
Cormac Toher, Corey Oses, David Hicks +48
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 d…
AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
Eric Gossett, Cormac Toher, Corey Oses +8
Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis. As a result, a p…
The AFLOW Standard for High-Throughput Materials Science Calculations
Camilo E. Calderon, Jose J. Plata, Cormac Toher +8
The Automatic-Flow ( AFLOW ) standard for the high-throughput construction of materials science electronic structure databases is described. Electronic structure calculations of so…