De, Sandip

Comparing molecules and solids across structural and alchemical space

De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016) · Physical Chemistry Chemical Physics

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 778

Machine learning unifies the modeling of materials and molecules

Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 764

A foundation model for atomistic materials chemistry

Batatia, I., Benner, P., Chiang, Y., Elena, A. M., Kovács, D. P., Riebesell, J., Advincula, X. R., Asta, M., Avaylon, M., Baldwin, W. J., et al. (2024) · arXiv

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 604

Machine learning for the structure–energy–property landscapes of molecular crystals

Musil, F., De, S., Yang, J., Campbell, J. E., Day, G. M., & Ceriotti, M. (2018) · Chemical Science

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 216

Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

Yang, J., De, S., Campbell, J. E., Li, S., Ceriotti, M., & Day, G. M. (2018) · Chemistry of Materials

Organic Electronics and PhotovoltaicsMachine Learning in Materials Science

Cited by 113

Mapping and classifying molecules from a high-throughput structural database

De, S., Musil, F., Ingram, T., Baldauf, C., & Ceriotti, M. (2017) · Journal of Cheminformatics

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 42