Ong, Shyue Ping

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

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Chen, C., Ye, W., Zuo, Y., Zheng, C., & Ong, S. P. (2019) · Chemistry of Materials

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 1,552

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

A universal graph deep learning interatomic potential for the periodic table

Chen, C., & Ong, S. P. (2022) · Nature Computational Science

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 1,140

Performance and Cost Assessment of Machine Learning Interatomic Potentials

Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., et al. (2020) · The Journal of Physical Chemistry A

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 968

A Critical Review of Machine Learning of Energy Materials

Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 609

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

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