A high-throughput infrastructure for density functional theory calculations

Generalized Gradient Approximation Made Simple

Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824

Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set

Kresse, G., & Furthmüller, J. (1996) · Physical Review B · doi:10.1103/physrevb.54.11169 · Cited by 123,652

Projector augmented-wave method

Blöchl, P. E. (1994) · Physical Review B · doi:10.1103/physrevb.50.17953 · Cited by 92,732

From ultrasoft pseudopotentials to the projector augmented-wave method

Kresse, G., & Joubert, D. (1999) · Physical Review B · doi:10.1103/physrevb.59.1758 · Cited by 85,307

Special points for Brillouin-zone integrations

Monkhorst, H. J., & Pack, J. D. (1976) · Physical Review B · doi:10.1103/physrevb.13.5188 · Cited by 71,721

Self-Consistent Equations Including Exchange and Correlation Effects

Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135

A new mixing of Hartree–Fock and local density-functional theories

Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464304 · Cited by 16,515

A relational model of data for large shared data banks

Codd, E. F. (1970) · Communications of the ACM · doi:10.1145/362384.362685 · Cited by 5,547

The entity-relationship model—toward a unified view of data

Chen, P. P.-S. (1976) · ACM Transactions on Database Systems · doi:10.1145/320434.320440 · Cited by 4,708

Computational high-throughput screening of electrocatalytic materials for hydrogen evolution

Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I., & Nørskov, J. K. (2006) · Nature Materials · doi:10.1038/nmat1752 · Cited by 4,097

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 · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Xie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., & Wolverton, C. (2013) · JOM · doi:10.1007/s11837-013-0755-4 · Cited by 2,584

The high-throughput highway to computational materials design

Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053

A general-purpose machine learning framework for predicting properties of inorganic materials

Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.28 · Cited by 1,831

Pseudopotentials for high-throughput DFT calculations

Garrity, K. F., Bennett, J. W., Rabe, K. M., & Vanderbilt, D. (2014) · Computational Materials Science · doi:10.1016/j.commatsci.2013.08.053 · Cited by 1,644

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 · doi:10.1021/acs.chemmater.9b01294 · Cited by 1,552

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., Taylor, R. H., Nelson, L. J., Hart, G. L. W., Sanvito, S., Buongiorno-Nardelli, M., et al. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.02.002 · Cited by 1,148

SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., & Ghiringhelli, L. M. (2018) · Physical Review Materials · doi:10.1103/physrevmaterials.2.083802 · Cited by 944

Data‐Driven Materials Science: Status, Challenges, and Perspectives

Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808

Predicting the state of charge and health of batteries using data-driven machine learning

Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J., & Seh, Z. W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0156-7 · Cited by 793

Machine learning for alloys

Hart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703

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 · doi:10.1002/aenm.201903242 · 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 · doi:10.1103/physrevb.85.155208 · Cited by 506

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289

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 · doi:10.1557/jmr.2016.80 · Cited by 244

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

Janet, J. P., Chan, L., & Kulik, H. J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00170 · Cited by 200

Opportunities and challenges of text mining in materials research

Kononova, O., He, T., Huo, H., Trewartha, A., Olivetti, E. A., & Ceder, G. (2021) · iScience · doi:10.1016/j.isci.2021.102155 · Cited by 182

Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases

Jain, D., Chaube, S., Khullar, P., Goverapet Srinivasan, S., & Rai, B. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp03240a · Cited by 105

Designer Matter: A perspective

Reis, P. M., Jaeger, H. M., & van Hecke, M. (2015) · Extreme Mechanics Letters · doi:10.1016/j.eml.2015.09.004 · Cited by 93

Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

Janet, J. P., Gani, T. Z. H., Steeves, A. H., Ioannidis, E. I., & Kulik, H. J. (2017) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.7b00808 · Cited by 56

Learn-and-Match Molecular Cations for Perovskites

Park, H., Mall, R., Alharbi, F. H., Sanvito, S., Tabet, N., Bensmail, H., & El-Mellouhi, F. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b06208 · Cited by 50

Improved physics-based structural descriptors of perovskite materials enable higher accuracy of machine learning

Li, C., Hao, H., Xu, B., Shen, Z., Zhou, E., Jiang, D., & Liu, H. (2021) · Computational Materials Science · doi:10.1016/j.commatsci.2021.110714 · Cited by 32

Accelerating materials discovery via AI-Agent integration of large language models and simulation tools

Wang, X., Zeng, Q., Xu, D.-H., Zhang, L., Jiang, G., & Yang, M. (2026) · Journal of Materials Informatics · doi:10.20517/jmi.2025.69 · Cited by 2