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
Showing 2020Show all
3 papers · 1 filter
cond-mat.mtrl-sci2020★ 3 cited
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…
physics.comp-ph2020★ 150 cited
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…
physics.flu-dyn2020★ 67 cited
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…