179 citations · 186 across the 4 of their papers we have counts for
4 papers
Machine-learned multi-system surrogate models for materials prediction
Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker +6
Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the…
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications
A. Hunter, B. A. Moore, M. K. Mudunuru +9
In this paper, five different approaches for reduced-order modeling of brittle fracture in geomaterials, specifically concrete, are presented and compared. Four of the five methods…
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…
A computational high-throughput search for new ternary superalloys
Chandramouli Nyshadham, Corey Oses, Jacob E. Hansen +3
In 2006, a novel cobalt-based superalloy was discovered [1] with mechanical properties better than some conventional nickel-based superalloys. As with conventional superalloys, its…