Pizzi, Giovanni

Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

Mounet, N., Gibertini, M., Schwaller, P., Campi, D., Merkys, A., Marrazzo, A., Sohier, T., Castelli, I. E., Cepellotti, A., Pizzi, G., et al. (2018) · Nature Nanotechnology

Machine Learning in Materials Science2D Materials and Applications

Cited by 1,863

How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

Bosoni, E., Beal, L., Bercx, M., Blaha, P., Blügel, S., Bröder, J., Callsen, M., Cottenier, S., Degomme, A., Dikan, V., et al. (2023) · Nature Reviews Physics

Advanced Chemical Physics StudiesMachine Learning in Materials Science

Cited by 89

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

Blum, V., Asahi, R., Autschbach, J., Bannwarth, C., Bihlmayer, G., Blügel, S., Burns, L. A., Crawford, T. D., Dawson, W., de Jong, W. A., et al. (2024) · Electronic Structure

Advanced Chemical Physics StudiesMachine Learning in Materials Science

Cited by 24