Hautier, Geoffroy

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 13,094

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science

Machine Learning in Materials ScienceAdvancements in Battery Materials

Cited by 4,534

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

Hautier, G., Fischer, C. C., Jain, A., Mueller, T., & Ceder, G. (2010) · Chemistry of Materials

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 580

Accuracy of density functional theory in predicting formation energies of ternary oxides from binary oxides and its implication on phase stability

Hautier, G., Ong, S. P., Jain, A., Moore, C. J., & Ceder, G. (2012) · Physical Review B

Advanced Chemical Physics StudiesMachine Learning in Materials Science

Cited by 506

Data Mined Ionic Substitutions for the Discovery of New Compounds

Hautier, G., Fischer, C., Ehrlacher, V., Jain, A., & Ceder, G. (2011) · Inorganic Chemistry

Machine Learning in Materials ScienceCrystal Structures and Properties

Cited by 394

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

Jain, A., Hautier, G., Ong, S. P., & Persson, K. (2016) · Journal of Materials Research

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 244

Combining phonon accuracy with high transferability in Gaussian approximation potential models

George, J., Hautier, G., Bartók, A. P., Csányi, G., & Deringer, V. L. (2020) · The Journal of Chemical Physics

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 44

MXene Anion Engineering for Efficient Hydrogen Evolution

Tiwari, A. P., McBride, S., Hamlin, A. B., Rahman, M. S., Huddy, J. E., Hautier, G., & Scheideler, W. J. (2023) · ACS Sustainable Chemistry & Engineering

Electrocatalysts for Energy ConversionMXene and MAX Phase Materials

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