Ceriotti, Michele

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

Barely porous organic cages for hydrogen isotope separation

Liu, M., Zhang, L., Little, M. A., Kapil, V., Ceriotti, M., Yang, S., Ding, L., Holden, D. L., Balderas-Xicohténcatl, R., He, D., et al. (2019) · Science

Membrane Separation and Gas TransportCovalent Organic Framework Applications

Cited by 399

Simplifying the representation of complex free-energy landscapes using sketch-map

Ceriotti, M., Tribello, G. A., & Parrinello, M. (2011) · Proceedings of the National Academy of Sciences

Protein Structure and DynamicsModel Reduction and Neural Networks

Cited by 340

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

Imbalzano, G., Anelli, A., Giofré, D., Klees, S., Behler, J., & Ceriotti, M. (2018) · The Journal of Chemical Physics

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 330

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

Grisafi, A., Wilkins, D. M., Csányi, G., & Ceriotti, M. (2018) · Physical Review Letters

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 309

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

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Wilkins, D. M., Grisafi, A., Yang, Y., Lao, K. U., DiStasio, R. A., & Ceriotti, M. (2019) · Proceedings of the National Academy of Sciences

Advanced Chemical Physics StudiesMachine Learning in Materials Science

Cited by 204

Using sketch-map coordinates to analyze and bias molecular dynamics simulations

Tribello, G. A., Ceriotti, M., & Parrinello, M. (2012) · Proceedings of the National Academy of Sciences

Protein Structure and DynamicsAdvanced Physical and Chemical Molecular Interactions

Cited by 202

Atom-density representations for machine learning

Willatt, M. J., Musil, F., & Ceriotti, M. (2019) · The Journal of Chemical Physics

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 179

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Ceriotti, M. (2019) · The Journal of Chemical Physics

Machine Learning in Materials ScienceAdvanced Physical and Chemical Molecular Interactions

Cited by 173

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

Nguyen, T. T., Székely, E., Imbalzano, G., Behler, J., Csányi, G., Ceriotti, M., Götz, A. W., & Paesani, F. (2018) · The Journal of Chemical Physics

Advanced Chemical Physics StudiesMachine Learning in Materials Science

Cited by 171

Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

Willatt, M. J., Musil, F., & Ceriotti, M. (2018) · Physical Chemistry Chemical Physics

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 141

Demonstrating the Transferability and the Descriptive Power of Sketch-Map

Ceriotti, M., Tribello, G. A., & Parrinello, M. (2013) · Journal of Chemical Theory and Computation

Model Reduction and Neural NetworksAdvanced Physical and Chemical Molecular Interactions

Cited by 126

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

Generalized convex hull construction for materials discovery

Anelli, A., Engel, E. A., Pickard, C. J., & Ceriotti, M. (2018) · Physical Review Materials

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 48

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