Levy, Ohad

The high-throughput highway to computational materials design

Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 2,053

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., Taylor, R. H., Nelson, L. J., Hart, G. L. W., Sanvito, S., Buongiorno-Nardelli, M., et al. (2012) · Computational Materials Science

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 1,148

Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases

Perim, E., Lee, D., Liu, Y., Toher, C., Gong, P., Li, Y., Simmons, W. N., Levy, O., Vlassak, J. J., Schroers, J., et al. (2016) · Nature Communications

Metallic Glasses and Amorphous AlloysMachine Learning in Materials Science

Cited by 139

AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis

Oses, C., Gossett, E., Hicks, D., Rose, F., Mehl, M. J., Perim, E., Takeuchi, I., Sanvito, S., Scheffler, M., Lederer, Y., et al. (2018) · Journal of Chemical Information and Modeling

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 108

AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes

Hicks, D., Toher, C., Ford, D. C., Rose, F., Santo, C. D., Levy, O., Mehl, M. J., & Curtarolo, S. (2021) · npj Computational Materials

Machine Learning in Materials ScienceCrystal Structures and Properties

Cited by 60