155 citations · 324 across the 4 of their papers we have counts for
4 papers
Deciphering Chemical Ordering in High Entropy Materials: A Machine Learning-Accelerated High-throughput Cluster Expansion Approach
Guillermo Vazquez, Daniel Sauceda, Raymundo Arróyave
The Cluster Expansion (CE) Method encounters significant computational challenges in multicomponent systems due to the computational expense of generating training data through den…
Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys
Guillermo Vazquez, Prashant Singh, Daniel Sauceda +5
We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high…
Machine-learning enabled thermodynamic model for the design of new rare-earth compounds
Prashant Singh, Tyler Del Rose, Guillermo Vazquez +2
We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning…
Towards Stacking Fault Energy Engineering in FCC High Entropy Alloys
Tasneem Khan, Tanner Kirk, Guillermo Vazquez +5
Stacking Fault Energy (SFE) is an intrinsic alloy property that governs much of the plastic deformation mechanisms observed in fcc alloys. While SFE has been recognized for many ye…