Ceder, Gerbrand

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

Oxidation energies of transition metal oxides within the GGA + U framework

Wang, L., Maxisch, T., & Ceder, G. (2006) · Physical Review B

Advanced Chemical Physics StudiesPhysics of Superconductivity and Magnetism

Cited by 2,785

Interface Stability in Solid-State Batteries

Richards, W. D., Miara, L. J., Wang, Y., Kim, J. C., & Ceder, G. (2016) · Chemistry of Materials

Advanced Battery Materials and TechnologiesAdvancements in Battery Materials

Cited by 1,652

Lithium-ion battery supply chain considerations: analysis of potential bottlenecks in critical metals

Olivetti, E. A., Ceder, G., Gaustad, G. G., & Fu, X. (2017) · Joule

Extraction and Separation ProcessesAdvancements in Battery Materials

Cited by 1,572

Unsupervised word embeddings capture latent knowledge from materials science literature

Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K. A., Ceder, G., & Jain, A. (2019) · Nature

Machine Learning in Materials ScienceTopic Modeling

Cited by 1,123

An autonomous laboratory for the accelerated synthesis of inorganic materials

Szymanski, N. J., Rendy, B., Fei, Y., Kumar, R. E., He, T., Milsted, D., McDermott, M. J., Gallant, M., Cubuk, E. D., Merchant, A., et al. (2023) · Nature

Machine Learning in Materials ScienceInorganic Chemistry and Materials

Cited by 1,041

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Deng, B., Zhong, P., Jun, K., Riebesell, J., Han, K., Bartel, C. J., & Ceder, G. (2023) · Nature Machine Intelligence

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 944

Structured information extraction from scientific text with large language models

Dagdelen, J., Dunn, A., Lee, S., Walker, N., Rosen, A. S., Ceder, G., Persson, K. A., & Jain, A. (2024) · Nature Communications

Topic ModelingMachine Learning in Materials Science

Cited by 771

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

Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning

Kim, E., Huang, K., Saunders, A., McCallum, A., Ceder, G., & Olivetti, E. (2017) · Chemistry of Materials

Machine Learning in Materials ScienceComputational and Text Analysis Methods

Cited by 473

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

Nucleation of metastable aragonite CaCO 3 in seawater

Sun, W., Jayaraman, S., Chen, W., Persson, K. A., & Ceder, G. (2015) · Proceedings of the National Academy of Sciences

Calcium Carbonate Crystallization and InhibitionCrystallization and Solubility Studies

Cited by 310

Data-driven materials research enabled by natural language processing and information extraction

Olivetti, E. A., Cole, J. M., Kim, E., Kononova, O., Ceder, G., Han, T. Y.-J., & Hiszpanski, A. M. (2020) · Applied Physics Reviews

Machine Learning in Materials ScienceComputational and Text Analysis Methods

Cited by 302

A critical examination of compound stability predictions from machine-learned formation energies

Bartel, C. J., Trewartha, A., Wang, Q., Dunn, A., Jain, A., & Ceder, G. (2020) · npj Computational Materials

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 277

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

de Jong, M., Chen, W., Notestine, R., Persson, K., Ceder, G., Jain, A., Asta, M., & Gamst, A. (2016) · Scientific Reports

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 243

Lithium superionic conductors with corner-sharing frameworks

Jun, K., Sun, Y., Xiao, Y., Zeng, Y., Kim, R., Kim, H., Miara, L. J., Im, D., Wang, Y., & Ceder, G. (2022) · Nature Materials

Advanced Battery Materials and TechnologiesAdvancements in Battery Materials

Cited by 236

Opportunities and challenges of text mining in materials research

Kononova, O., He, T., Huo, H., Trewartha, A., Olivetti, E. A., & Ceder, G. (2021) · iScience

Machine Learning in Materials ScienceBiomedical Text Mining and Ontologies

Cited by 182

Semi-supervised machine-learning classification of materials synthesis procedures

Huo, H., Rong, Z., Kononova, O., Sun, W., Botari, T., He, T., Tshitoyan, V., & Ceder, G. (2019) · npj Computational Materials

Machine Learning in Materials ScienceComputational and Text Analysis Methods

Cited by 151

Structured information extraction from complex scientific text with fine-tuned large language models

Dunn, A., Dagdelen, J., Walker, N., Lee, S., Rosen, A. S., Ceder, G., Persson, K., & Jain, A. (2022) · arXiv

Topic ModelingMachine Learning in Materials Science

Cited by 67

Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO3

Cruse, K., Baibakova, V., Abdelsamie, M., Hong, K., Bartel, C. J., Trewartha, A., Jain, A., Sutter-Fella, C. M., & Ceder, G. (2024) · Chemistry of Materials

Machine Learning in Materials ScienceMultiferroics and related materials

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