226 citations
- Argonne National LaboratoryUS2 papers
- Fritz Haber Institute of the Max Planck SocietyDE2 papers
- SLAC National Accelerator LaboratoryUS2 papers
- University of CambridgeGB2 papers
- Brigham Young UniversityUS1 paper
- Carnegie Mellon UniversityUS1 paper
- City College of New YorkUS1 paper
- Cornell High Energy Synchrotron Source1 paper
- Cornell UniversityUS1 paper
- Franklin W. Olin College of EngineeringUS1 paper
- Johns Hopkins UniversityUS1 paper
- Material Measurement LaboratoryUS1 paper
13 papers
Predicting Magnetic Janus Particle Assembly with Differential Evolution Algorithm
Eric A. McPherson, Kenneth Kroenlein, Ilona Kretzschmar
Magnetic Janus particles allow access to complex, nonlinear assembled structures that may enable interesting new magnetorheological (MR) fluids with uniquely engineered field respo…
Quantifying the performance of machine learning models in materials discovery
Christopher K. H. Borg, Eric S. Muckley, Clara Nyby +4
The predictive capabilities of machine learning (ML) models used in materials discovery are typically measured using simple statistics such as the root-mean-square error (RMSE) or…
Mapping Thermoelectric Transport in a Multicomponent Alloy Space
Ramya Gurunathan, Suchismita Sarker, Christopher K. H. Borg +4
Interest in high entropy alloy thermoelectric materials is predicated on achieving ultralow lattice thermal conductivity through large compositional disorder. However, h…
Quantifying uncertainty in high-throughput density functional theory: a comparison of AFLOW, Materials Project, and OQMD
Vinay I. Hegde, Christopher K. H. Borg, Zachary del Rosario +7
A central challenge in high throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and post-processing techniques that can be use…
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Marcel F. Langer, Alex Goeßmann, Matthias Rupp
Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise…
Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling
Pedro M. Milani, Julia Ling, John K. Eaton
A cylindrical and inclined jet in crossflow is studied under two distinct velocity ratios, and , using highly resolved large eddy simulations (LES). First, an investigat…