id: 9
Nov. 19, 2021
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AFLOW ML machine learning predictor

Duke University | 2020

Stefano Curtarolo;

Description:

Property Labeled Material Fragments (PLMF) Predicts the electronic and thermomechanical properties of a crystal. Molar Fragment Descriptor (MFD) Predicts the vibrational free energies (Fvib) and entropies (Svib) of a crystal. AFLOW Superconductor (ASC) Classifies a material as a superconductor and predicts the critical temperature (TC).

Citation:

Toher, Cormac, Corey Oses, David Hicks, Eric Gossett, Frisco Rose, Pinku Nath, Demet Usanmaz et al. "The AFLOW fleet for materials discovery." Handbook of Materials Modeling: Methods: Theory and Modeling (2020): 1785-1812.

Keywords:

electronic and thermomechanical, vibrational free energies, entropies, superconductor, critical temperature,

Curator: hujianju@gmail.com