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 2017Show all
3 papers · 1 filter
cs.AI2017★ 6 cited
Building Data-driven Models with Microstructural Images: Generalization and Interpretability
Julia Ling, Maxwell Hutchinson, Erin Antono +3
As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the…
cond-mat.mtrl-sci2017★ 71 cited
Robust FCC solute diffusion predictions from ab-initio machine learning methods
Henry Wu, Aren Lorenson, Ben Anderson +4
We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous de…
stat.ML2017★ 180 cited
High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates
Julia Ling, Max Hutchinson, Erin Antono +2
The optimization of composition and processing to obtain materials that exhibit desirable characteristics has historically relied on a combination of scientist intuition, trial and…