35 citations · 105 across the 13 of their papers we have counts for
18 papers · 1 filter
By how much can closed-loop frameworks accelerate computational materials discovery?
Lance Kavalsky, Vinay I. Hegde, Eric Muckley +3
The implementation of automation and machine learning surrogatization within closed-loop computational workflows is an increasingly popular approach to accelerate materials discove…
Effect of disorder and doping on electronic structure and diffusion properties of LiVO
Mohammad Babar, Hasnain Hafiz, Zeeshan Ahmad +3
VO in its phase (LiVO) with excess lithium is a potential alternative to the graphite anode for lithium-ion batteries at low temperature and fast…
MeltNet: Predicting alloy melting temperature by machine learning
Pin-Wen Guan, Venkatasubramanian Viswanathan
Thermodynamics is fundamental for understanding and synthesizing multi-component materials, while efficient and accurate prediction of it still remain urgent and challenging. As a…
Robust Active Site Design of Single Atom Catalysts for Electrochemical Ammonia Synthesis
Lance Kavalsky, Venkatasubramanian Viswanathan
In this work, we provide a computational methodological framework using the single-atom systems as an example material class for ammonia synthesis that is robust towards parameter…
Uncertainty quantification in first-principles predictions of phonon properties and lattice thermal conductivity
Holden L. Parks, Hyun-Young Kim, Venkatasubramanian Viswanathan +1
We present a framework for quantifying the uncertainty that results from the choice of exchange-correlation (XC) functional in predictions of phonon properties and thermal conducti…
Machine Learning Enabled Discovery of Application Dependent Design Principles for Two-dimensional Materials
Victor Venturi, Holden Parks, Zeeshan Ahmad +1
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calcul…