Jain, Anubhav

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

Recent advances and applications of deep learning methods in materials science

Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 1,172

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

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

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Dunn, A., Wang, Q., Ganose, A., Dopp, D., & Jain, A. (2020) · npj Computational Materials

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 411

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

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

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

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

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

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

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

Cited by 13