83 citations · 334 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus Ab Initio Methods
Fleur Legrain, Jesús Carrete, Ambroise van Roekeghem +2
Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability o…
How the Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids
Fleur Legrain, Jesús Carrete, Ambroise van Roekeghem +2
Computing vibrational free energies () and entropies () has posed a long standing challenge to the high-throughput ab initio investigation of finite temperature p…