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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Open accessAbstract
Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design.
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“Instead, computational approaches championed by the Materials Project (MP)16, the Open Quantum Materials Database (OQMD)17, AFLOWLIB20 and NOMAD21 have used first-principles calculations based on density functional theory (DFT) as approximations of physical energies.”
From Scaling deep learning for materials discovery (2023) · cites this paper as [16]
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“while further structural details of more than 2000 of these new compounds have been placed in the GNoME Explorer archive3 within the Materials Project.4”
From Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery (2024) · cites this paper as [4]
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“Assuming future advances in materials science, ML research could potentially draw upon open databases such as the Materials Project [380] and the UCI Machine Learning Repository [271] to invent new, climate-friendly materials [830].”
From Tackling climate change with machine learning (2023) · cites this paper as [380]
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Cites
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
Kresse, G., & Furthmüller, J. (1996) · Computational Materials Science · doi:10.1016/0927-0256(96)00008-0 · Cited by 76,871
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
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541
Density-Functional Theory for Time-Dependent Systems
Runge, E., & Gross, E. K. U. (1984) · Physical Review Letters · doi:10.1103/physrevlett.52.997 · Cited by 8,914
Accurate Band Gaps of Semiconductors and Insulators with a Semilocal Exchange-Correlation Potential
Tran, F., & Blaha, P. (2009) · Physical Review Letters · doi:10.1103/physrevlett.102.226401 · Cited by 6,135
Hedin, L. (1965) · Physical Review · doi:10.1103/physrev.139.a796 · Cited by 5,561
Van der Waals density functionals applied to solids
Klimeš, J., Bowler, D. R., & Michaelides, A. (2011) · Physical Review B · doi:10.1103/physrevb.83.195131 · Cited by 4,624
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
Self-citationComputational 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
Flagged first, then by citations
Research works printed but not linked: 3
- U.S.D. of Energy, 2010, Department of Energy Workshop: Computational Materials Science and Chemistry for Innovation
- 2012, Proceedings of the 5th Workshop on Many-Task Computing on Grids and Supercomputers (MTAGS)
- 1984, Russ. Metall., 2, 202
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Miracle, D. B., & Senkov, O. N. (2016) · Acta Materialia · doi:10.1016/j.actamat.2016.08.081 · Cited by 9,065
Li-ion battery materials: present and future
Nitta, N., Wu, F., Lee, J. T., & Yushin, G. N. (2014) · Materials Today · doi:10.1016/j.mattod.2014.10.040 · Cited by 7,659
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Data-driven prediction of battery cycle life before capacity degradation
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228
Xie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828
Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,786
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
Kirklin, S. J., Saal, J. E., Meredig, B., Thompson, A. E., Doak, J. W., Aykol, M., Rühl, S., & Wolverton, C. M. (2015) · npj Computational Materials · doi:10.1038/npjcompumats.2015.10 · Cited by 2,464
From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis
Medford, A. J., Vojvodić, A., Hummelshøj, J. S., Voss, J., Abild‐Pedersen, F., Studt, F., Bligaard, T., Nilsson, A., & Nørskov, J. K. (2015) · Journal of Catalysis · doi:10.1016/j.jcat.2014.12.033 · Cited by 2,309
SchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217
Alloy Negative Electrodes for Li-Ion Batteries
Obrovac, M. N., & Chevrier, V. L. (2014) · Chemical Reviews · doi:10.1021/cr500207g · Cited by 2,228
Negating interfacial impedance in garnet-based solid-state Li metal batteries
Han, X., Gong, Y., Fu, K. K., He, X., Hitz, G. T., Dai, J., Pearse, A. J., Liu, B., Wang, H., Rubloff, G. W., et al. (2016) · Nature Materials · doi:10.1038/nmat4821 · Cited by 2,031
Zhu, Y., He, X., & Mo, Y. (2015) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.5b07517 · Cited by 1,942
Mounet, N., Gibertini, M., Schwaller, P., Campi, D., Merkys, A., Marrazzo, A., Sohier, T., Castelli, I. E., Cepellotti, A., Pizzi, G., et al. (2018) · Nature Nanotechnology · doi:10.1038/s41565-017-0035-5 · Cited by 1,863
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
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Togo, A. (2023) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.92.012001 · Cited by 1,865
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Hwang, J., Rao, R. R., Giordano, L., Katayama, Y., Yu, Y., & Shao‐Horn, Y. (2017) · Science · doi:10.1126/science.aam7092 · Cited by 1,817
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Richards, W. D., Miara, L. J., Wang, Y., Kim, J. C., & Ceder, G. (2016) · Chemistry of Materials · doi:10.1021/acs.chemmater.5b04082 · Cited by 1,652
Self-citationReproducibility in density functional theory calculations of solids
Lejaeghere, K., Bihlmayer, G., Björkman, T., Blaha, P., Blügel, S., Blum, V., Caliste, D., Castelli, I. E., Clark, S. J., Dal Corso, A., et al. (2016) · Science · doi:10.1126/science.aad3000 · Cited by 1,649
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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
Self-citationNelson, R., Ertural, C., George, J., Deringer, V. L., Hautier, G., & Dronskowski, R. (2020) · Journal of Computational Chemistry · doi:10.1002/jcc.26353 · Cited by 1,647
Self-citationWang, M., Jiang, C., Zhang, S., Song, X., Tang, Y., & Cheng, H. (2018) · Nature Chemistry · doi:10.1038/s41557-018-0045-4 · Cited by 1,594
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Montoya, J. H., Seitz, L. C., Chakthranont, P., Vojvodić, A., Jaramillo, T. F., & Nørskov, J. K. (2016) · Nature Materials · doi:10.1038/nmat4778 · Cited by 1,589
Aboulkhair, N. T., Simonelli, M., Parry, L., Ashcroft, I., Tuck, C. J., & Hague, R. (2019) · Progress in Materials Science · doi:10.1016/j.pmatsci.2019.100578 · Cited by 1,622
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Zhong, M., Tran, K., Min, Y., Wang, C., Wang, Z., Dinh, C., Luna, P. D., Yu, Z., Rasouli, A. S., Brodersen, P. M., et al. (2020) · Nature · doi:10.1038/s41586-020-2242-8 · Cited by 1,567
ELATE: an open-source online application for analysis and visualization of elastic tensors
Gaillac, R., Pullumbi, P., & Coudert, F. (2016) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/28/27/275201 · Cited by 1,536
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Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023) · Nature · doi:10.1038/s41586-023-06735-9 · Cited by 1,527
“To compute decomposition energies and count the total number of stable crystals relative to previous work 16,17 in a consistent fashion, we recalculated energies of all stable materials in the Materials Project and the OQMD with identical, updated DFT settings as enabled by pymatgen.”
MethodAccurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation
Furness, J. W., Kaplan, A. D., Ning, J., Perdew, J. P., & Sun, J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02405 · Cited by 1,438
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Ross, M. B., De Luna, P., Li, Y., Dinh, C.-T., Kim, D., Yang, P., & Sargent, E. H. (2019) · Nature Catalysis · doi:10.1038/s41929-019-0306-7 · Cited by 1,302
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Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276
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Bradlyn, B., Cano, J., Wang, Z., Vergniory, M. G., Felser, C., Cava, R. J., & Bernevig, B. A. (2016) · Science · doi:10.1126/science.aaf5037 · Cited by 1,252
Haastrup, S., Strange, M., Pandey, M., Deilmann, T., Schmidt, P. S., Hinsche, N. F., Gjerding, M. N., Torelli, D., Larsen, P. M., Riis-Jensen, A. C., et al. (2018) · 2D Materials · doi:10.1088/2053-1583/aacfc1 · Cited by 1,270
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Deringer, V. L., Bartók, A. P., Bernstein, N., Wilkins, D. M., Ceriotti, M., & Cśanyi, G. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00022 · Cited by 1,311
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Xiao, Y., Wang, Y. E., Bo, S., Kim, J. C., Miara, L. J., & Ceder, G. (2019) · Nature Reviews Materials · doi:10.1038/s41578-019-0157-5 · Cited by 1,264
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Kumar, M. H., Sabba, D., Leong, W. L., Boix, P. P., Prabhakar, R. R., Baikie, T., Chen, S., Ding, H. Q., Ramesh, R., Asta, M., et al. (2014) · Advanced Materials · doi:10.1002/adma.201401991 · Cited by 1,213
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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 · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
Self-citationLead-free germanium iodide perovskite materials for photovoltaic applications
Thirumal, K., Ding, H. Q., Chen, Y., Leong, W. L., Baikie, T., Zhang, Z., Sherburne, M., Li, S., Asta, M., Mathews, N., et al. (2015) · Journal of Materials Chemistry A · doi:10.1039/c5ta05741h · Cited by 1,169
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Canepa, P., Gautam, G. S., Hannah, D. C., Malik, R., Liu, M., Gallagher, K. G., Persson, K. A., & Ceder, G. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00614 · Cited by 1,185
Self-citationUnsupervised 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 · doi:10.1038/s41586-019-1335-8 · Cited by 1,123
Self-citationProgress in discovery and structural design of color conversion phosphors for LEDs
Xia, Z., & Liu, Q. (2016) · Progress in Materials Science · doi:10.1016/j.pmatsci.2016.09.007 · Cited by 1,095
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Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069
Matminer: An open source toolkit for materials data mining
Ward, L. T., Dunn, A., Faghaninia, A., Zimmermann, N., Bajaj, S., Wang, Q. S., Montoya, J. H., Chen, J., Bystrom, K., Dylla, M. T., et al. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.05.018 · Cited by 1,145
Self-citationSurface energies of elemental crystals
Tran, R., Xu, Z., Radhakrishnan, B., Winston, D., Sun, W. Q., Persson, K. A., & Ong, S. P. (2016) · Scientific Data · doi:10.1038/sdata.2016.80 · Cited by 1,080
Self-citationZhu, Y., He, X., & Mo, Y. (2015) · Journal of Materials Chemistry A · doi:10.1039/c5ta08574h · Cited by 1,047
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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 · doi:10.1038/s41586-023-06734-w · Cited by 1,041
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Sun, W. Q., Dacek, S. T., Ong, S. P., Hautier, G., Jain, A., Richards, W. D., Gamst, A. C., Persson, K. A., & Ceder, G. (2016) · Science Advances · doi:10.1126/sciadv.1600225 · Cited by 955
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Tran, K., & Ulissi, Z. W. (2018) · Nature Catalysis · doi:10.1038/s41929-018-0142-1 · Cited by 920
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Wang, J., Gao, Y., Kong, H., Kim, J., Choi, S., Francesco Ciucci, A., Hao, Y. M., Yang, S., Shao, Z., & Lim, J. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00575d · Cited by 896
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Zeier, W. G., Zevalkink, A., Gibbs, Z. M., Hautier, G., Kanatzidis, M. G., & Snyder, G. J. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201508381 · Cited by 881
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Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
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Reiser, P., Neubert, M., Eberhard, A., Torresi, L., Zhou, C., Shao, C., Metni, H., van Hoesel, C., Schopmans, H., Sommer, T., et al. (2022) · Communications Materials · doi:10.1038/s43246-022-00315-6 · Cited by 976
Brandt, R. E., Stevanović, V., Ginley, D. S., & Buonassisi, T. (2015) · MRS Communications · doi:10.1557/mrc.2015.26 · Cited by 844
Lookman, T., Balachandran, P. V., Xue, D., & Yuan, R. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0153-8 · Cited by 983
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Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
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Himanen, L., Jäger, M. O. J., Morooka, E. V., Canova, F. F., Ranawat, Y. S., Gao, D. Z., Rinke, P., & Foster, A. S. (2019) · Computer Physics Communications · doi:10.1016/j.cpc.2019.106949 · Cited by 901
Fu, K. K., Gong, Y., Liu, B., Zhu, Y., Xu, S., Yao, Y., Luo, W. H., Wang, C., Lacey, S. D., Dai, J., et al. (2017) · Science Advances · doi:10.1126/sciadv.1601659 · Cited by 853
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Hinuma, Y., Pizzi, G., Kumagai, Y., Oba, F., & Tanaka, I. (2016) · Computational Materials Science · doi:10.1016/j.commatsci.2016.10.015 · Cited by 838
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Fan, X., Ji, X. Y., Han, F., Yue, J., Chen, J., Chen, L., Deng, T., Jiang, J., & Wang, C. (2018) · Science Advances · doi:10.1126/sciadv.aau9245 · Cited by 850
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Witting, I. T., Chasapis, T. C., Ricci, F., Peters, M. C., Heinz, N. A., Hautier, G., & Snyder, G. J. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800904 · Cited by 842
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Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
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Singh, A. K., Mathew, K., Zhuang, H. L., & Hennig, R. G. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/jz502646d · Cited by 833
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Choudhary, K. K., & DeCost, B. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00650-1 · Cited by 843
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Gonze, X., Amadon, B., Antonius, G., Arnardi, F., Baguet, L., Beuken, J.-M., Bieder, J., François Bottin, A., Bouchet, J., Bousquet, É., et al. (2019) · Computer Physics Communications · doi:10.1016/j.cpc.2019.107042 · Cited by 779
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Chayambuka, K., Mulder, G., Danilov, D. L., & Notten, P. H. L. (2018) · Advanced Energy Materials · doi:10.1002/aenm.201800079 · Cited by 733
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Karlsson, R. K. B., & Cornell, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00389 · Cited by 733
Color-stable highly luminescent sky-blue perovskite light-emitting diodes
Xing, J., Zhao, Y., Askerka, M., Quan, L. N., Gong, X., Zhao, W., Zhao, J., Tan, H., Long, G., Gao, L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05909-8 · Cited by 721
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Fukuda, K., Yu, K., & Someya, T. (2020) · Advanced Energy Materials · doi:10.1002/aenm.202000765 · Cited by 714
Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112128 · Cited by 711
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Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
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Ganose, A. M., Park, J., Faghaninia, A., Woods‐Robinson, R., Persson, K. A., & Jain, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22440-5 · Cited by 710
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Guo, K., Yang, Z., Yu, C., & Buehler, M. J. (2020) · Materials Horizons · doi:10.1039/d0mh01451f · Cited by 732
Wang, S., Bai, Q., Nolan, A. M., Liu, Y., Gong, S., Sun, Q., & Mo, Y. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201901938 · Cited by 667
Yang, H., Wu, H., Ge, M., Li, L., Yuan, Y., Yao, Q., Chen, J. Y., Xia, L., Zheng, J., Chen, Z., et al. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201808825 · Cited by 635
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Toyao, T., Maeno, Z., Takakusagi, S., Kamachi, T., Takigawa, I., & Shimizu, K.-I. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04186 · Cited by 617
Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing
Rao, F., Ding, K., Zhou, Y., Zheng, Y., Xia, M., Lv, S., Song, Z., Feng, S., Ronneberger, I., Mazzarello, R., et al. (2017) · Science · doi:10.1126/science.aao3212 · Cited by 614
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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
Self-citationSolid Lubrication with MoS2: A Review
Vazirisereshk, M. R., Martini, A., Strubbe, D. A., & Baykara, M. Z. (2019) · Lubricants · doi:10.3390/lubricants7070057 · Cited by 608
Active learning of uniformly accurate interatomic potentials for materials simulation
Zhang, L., Lin, D.-Y., Wang, H., Car, R., & Weinan, E. (2019) · Physical Review Materials · doi:10.1103/physrevmaterials.3.023804 · Cited by 623
Swain, M. C., & Cole, J. M. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00207 · Cited by 598
A foundation model for atomistic materials chemistry
Batatia, I., Benner, P., Chiang, Y., Elena, A. M., Kovács, D. P., Riebesell, J., Advincula, X. R., Asta, M., Avaylon, M., Baldwin, W. J., et al. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0297006 · Cited by 1,147
“SW, AB and TV acknowledge the Pioneer Center for Accelerating P2X Materials discovery (CAPeX), DNRF Grant number P3.”
InfluentialSelf-citationPark, K. H., Bai, Q., Kim, D. H., Oh, D. Y., Zhu, Y., Mo, Y., & Jung, Y. S. (2018) · Advanced Energy Materials · doi:10.1002/aenm.201800035 · Cited by 594
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Zhou, S., Shi, J., Liu, S., Li, G., Pei, F., Chen, Y., Deng, J., Zheng, Q., Li, J., Zhao, C., et al. (2023) · Nature · doi:10.1038/s41586-023-06326-8 · Cited by 580
Metadata
- Authors
- Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- ORCID
- Venue
- APL Materials
- Publisher
- AIP Publishing
- Published
- 2013-07-01
- Volume
- 1
- Issue
- 1
- ISSN
- 2166-532X (ISSN-L)
- OpenAlex
- W1992985800
- Semantic Scholar
- 98476502ec663771d41d2f8c948fd176257f17bd
- Cited by
- 13,094
- References
- 59
- Citations by year
- 2026: 1877 · 2025: 2248 · 2024: 2086 · 2023: 1534 · 2022: 1425 · 2021: 1182 · 2020: 902 · 2019: 619 · 2018: 462 · 2017: 308 · 2016: 235 · 2015: 145 · 2014: 64 · 2013: 4
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- APL Materials (publishedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Digital Access to Libraries (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Energy (-AC02-05CH11231, DEFG0296ER45571, DE‐FG02‐, 05CH11231, DE AC02 05CH11231, FG0296ER45571, DE-AC02-)
- National Energy Research Scientific Computing Center
- European Commission (321988, DE-AC02-05CH11231)
- Fonds De La Recherche Scientifique - FNRS
- Basic Energy Sciences (DE-AC02, DE-AC02-05CH11231., DE-FG02, AC02–05CH11231)
- Lawrence Berkeley National Laboratory (AC02‐05CH11231, DE-AC02-05CH11231, 05CH11231)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- Materials Project, Materials Genome Initiative, inorganic materials, high-throughput computing, materials property prediction, materials discovery
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