Sanvito, Stefano

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

Accelerated discovery of new magnets in the Heusler alloy family

Sanvito, S., Oses, C., Xue, J., Tiwari, A., Zic, M., Archer, T., Tozman, P., Venkatesan, M., Coey, M., & Curtarolo, S. (2017) · Science Advances

Heusler alloys: electronic and magnetic propertiesMachine Learning in Materials Science

Cited by 287

Zeeman splitting and dynamical mass generation in Dirac semimetal ZrTe5

Liu, Y., Yuan, X., Zhang, C., Jin, Z., Narayan, A., Luo, C., Chen, Z., Yang, L., Zou, J., Wu, X., et al. (2016) · Nature Communications

Topological Materials and PhenomenaAdvanced Condensed Matter Physics

Cited by 202

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

Topological tuning in three-dimensional dirac semimetals.

Narayan, A., Di Sante, D., Picozzi, S., & Sanvito, S. (2014) · Physical Review Letters

Topological Materials and Phenomena2D Materials and Applications

Cited by 61

Learn-and-Match Molecular Cations for Perovskites

Park, H., Mall, R., Alharbi, F. H., Sanvito, S., Tabet, N., Bensmail, H., & El-Mellouhi, F. (2019) · The Journal of Physical Chemistry A

Machine Learning in Materials SciencePerovskite Materials and Applications

Cited by 50

Importance of structural deformation features in the prediction of hybrid perovskite bandgaps

Park, H., Mall, R., Ali, A., Sanvito, S., Bensmail, H., & El-Mellouhi, F. (2020) · Computational Materials Science

Machine Learning in Materials SciencePerovskite Materials and Applications

Cited by 39

Surfing Multiple Conformation-Property Landscapes via Machine Learning: Designing Single-Ion Magnetic Anisotropy

Lunghi, A., & Sanvito, S. (2020) · The Journal of Physical Chemistry C

Magnetism in coordination complexesMachine Learning in Materials Science

Cited by 35