65 citations · 215 across the 15 of their papers we have counts for
8 papers · 2 filters
Machine learning, phase stability, and disorder with the Automatic Flow Framework for Materials Discovery
Corey Oses
Traditional materials discovery approaches - relying primarily on laborious experiments - have controlled the pace of technology. Instead, computational approaches offer an acceler…
High-entropy high-hardness metal carbides discovered by entropy descriptors
Pranab Sarker, Tyler Harrington, Cormac Toher +6
High-entropy materials have attracted considerable interest due to the combination of useful properties and promising applications. Predicting their formation remains the major hin…
Coordination corrected ab initio formation enthalpies
Rico Friedrich, Demet Usanmaz, Corey Oses +5
The correct calculation of formation enthalpy is one of the enablers of ab-initio computational materials design. For several classes of systems (e.g. oxides) standard density func…
AFLOW-QHA3P: Robust and automated method to compute thermodynamic properties of solids
Pinku Nath, Demet Usanmaz, David Hicks +5
Accelerating the calculations of finite-temperature thermodynamic properties is a major challenge for rational materials design. Reliable methods can be quite expensive, limiting t…
AFLOW-CHULL: Cloud-oriented platform for autonomous phase stability analysis
Corey Oses, Eric Gossett, David Hicks +10
prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically-competing structures at formation conditions. Lar…
Automated computation of materials properties
Cormac Toher, Corey Oses, Stefano Curtarolo
Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functiona…