42 citations · 73 across the 2 of their papers we have counts for
5 papers
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
Dipendra Jha, Logan Ward, Zijiang Yang +5
Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applyi…
A Data Ecosystem to Support Machine Learning in Materials Science
Ben Blaiszik, Logan Ward, Marcus Schwarting +5
Facilitating the application of machine learning to materials science problems will require enhancing the data ecosystem to enable discovery and collection of data from many source…
Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction
Maximilian Amsler, Logan Ward, Vinay I. Hegde +3
We report the computational investigation of a series of ternary XYZ and XYZ compounds with X={Mg, Ca, Sr, Ba}, Y={P, As, Sb, Bi}, and Z={S, Se, Te}. The compos…
DLHub: Model and Data Serving for Science
Ryan Chard, Zhuozhao Li, Kyle Chard +7
While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermo…
Rapid Production of Accurate Embedded-Atom Method Potentials for Metal Alloys
Logan Ward, Anupriya Agrawal, Katharine M. Flores +1
The most critical limitation to the wide-scale use of classical molecular dynamics for alloy design is the availability of suitable interatomic potentials. In this work, we demonst…