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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
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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
Cited by
Sorted by citations
Items 701–800 of 1,040
Cho, S. B., & Mishra, R. (2018) · Applied Physics Letters · doi:10.1063/1.5019721 · Cited by 143
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Kim, E., Jensen, Z., van Grootel, A., Huang, K., Staib, M., Mysore, S., Chang, H.-S., Strubell, E., McCallum, A., Jegelka, S., et al. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00995 · Cited by 151
Diversity-oriented synthesis of polymer membranes with ion solvation cages
Baran, M. J., Carrington, M. E., Sahu, S., Baskin, A., Song, J., Baird, M. A., Han, K. S., Mueller, K. T., Teat, S. J., Meckler, S. M., et al. (2021) · Nature · doi:10.1038/s41586-021-03377-7 · Cited by 148
Addressing energy storage needs at lower cost via on-site thermal energy storage in buildings
Odukomaiya, A., Woods, J., James, N., Kaur, S., Gluesenkamp, K. R., Kumar, N., Mumme, S., Jackson, R. K., & Prasher, R. (2021) · Energy & Environmental Science · doi:10.1039/d1ee01992a · Cited by 144
He, J., Xia, Y., Lin, W., et al. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202108532 · Cited by 143
Fast Ion Conduction and Its Origin in Li6–xPS5–xBr1+x
Wang, P., Liu, H., Patel, S. V., et al. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b05331 · Cited by 142
Liu, C., Nikolaev, S. A., Ren, W., & Burton, L. A. (2020) · Journal of Materials Chemistry C · doi:10.1039/d0tc01165g · Cited by 142
Park, D., Park, H., Lee, Y., Kim, S., Jung, H., Chung, K. Y., Shim, J. H., & Yu, S. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c07003 · Cited by 143
Accelerating materials discovery with Bayesian optimization and graph deep learning
Zuo, Y., Qin, M., Chen, C., Ye, W., Li, X., Luo, J., & Ong, S. P. (2021) · Materials Today · doi:10.1016/j.mattod.2021.08.012 · Cited by 142
Self-citationYu, X., Araujo, R. B., Qiu, Z., dos Santos, E. C., Anil, A., Cornell, A., Pettersson, L. G. M., & Johnsson, M. (2022) · Advanced Energy Materials · doi:10.1002/aenm.202103750 · Cited by 143
AI methods in materials design, discovery and manufacturing: A review
Papadimitriou, I., Gialampoukidis, I., Vrochidis, S., & Kompatsiaris, I. Y. (2024) · Computational Materials Science · doi:10.1016/j.commatsci.2024.112793 · Cited by 156
Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
Medasani, B., Gamst, A. C., Ding, H. Q., Chen, W., Persson, K. A., Asta, M., Canning, A., & Harańczyk, M. (2016) · npj Computational Materials · doi:10.1038/s41524-016-0001-z · Cited by 142
Self-citationHow Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids
Legrain, F., Carrete, J., van Roekeghem, A., Stefano Curtarolo, A., & Mingo, N. (2017) · Chemistry of Materials · doi:10.1021/acs.chemmater.7b00789 · Cited by 141
Hinuma, Y., Hayashi, H., Kumagai, Y., Tanaka, I., & Oba, F. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.96.094102 · Cited by 142
Li, H., Tang, H., Ma, C., et al. (2018) · Chemistry of Materials · doi:10.1021/acs.chemmater.7b03903 · Cited by 141
Self-citationKochetkov, I. R., Zuo, T., Rueß, R., Singh, B., Zhou, L., Kaup, K., Janek, J., & Nazar, L. F. (2022) · Energy & Environmental Science · doi:10.1039/d2ee00803c · Cited by 141
Chemical reaction networks and opportunities for machine learning
Wen, M., Spotte‐Smith, E. W. C., Blau, S. M., McDermott, M. J., Krishnapriyan, A. S., & Persson, K. A. (2023) · Nature Computational Science · doi:10.1038/s43588-022-00369-z · Cited by 147
“…materials synthesis pathways, McDermott et al. 18 used a combinatorial, filter-based approach for reaction enumeration from a known set of material compositions; they then determined reaction free energies by referencing DFT-calculated formation energies in the Materials Project database 52 .”
MethodSelf-citationUnke, O. T., Stöhr, M., Ganscha, S., Unterthiner, T., Maennel, H., Kashubin, S., Ahlin, D., Gastegger, M., Sandonas, L. M., Berryman, J. T., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn4397 · Cited by 160
High-throughput computational X-ray absorption spectroscopy
Mathew, K., Zheng, C., Winston, D., Chen, C., Dozier, A., Rehr, J. J., Ong, S. P., & Persson, K. A. (2018) · Scientific Data · doi:10.1038/sdata.2018.151 · Cited by 141
Self-citationExploration for Two-Dimensional Electrides via Database Screening and Ab Initio Calculation
Inoshita, T., Jeong, S., Hamada, N., et al. (2014) · Physical Review X · doi:10.1103/physrevx.4.031023 · Cited by 140
Stoll, A., & Benner, P. (2021) · GAMM-Mitteilungen · doi:10.1002/gamm.202100003 · Cited by 151
“Existing databases are the materials project [63, 98], the inorganic crystal structure database [39, 126], the Materials genome initiative [155, 171] ,the NOMAD archive [34, 113], the Topological Materials Database [156], Supercon [97], or the National Institute of Materials Science 2011 [111] with many databases of m…”
MethodBenayad, A., Diddens, D., Heuer, A., Krishnamoorthy, A. N., Maiti, M., Cras, F. L., Legallais, M., Rahmanian, F., Shin, Y., Stein, H. S., et al. (2021) · Advanced Energy Materials · doi:10.1002/aenm.202102678 · Cited by 147
Ganguly, P., Mathew, S., Clarizia, L., Syam Kumar, R., Akande, A., Hinder, S. J., Breen, A., & Pillai, S. C. (2019) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2019.04.033 · Cited by 138
Zhang, T., Cai, Z., & Chen, S. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c03622 · Cited by 140
Design and development of Ti–Zr–Hf–Nb–Ta–Mo high-entropy alloys for metallic biomaterials
Iijima, Y., Nagase, T., Matsugaki, A., et al. (2021) · Materials & Design · doi:10.1016/j.matdes.2021.109548 · Cited by 139
Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
Peng, J., Schwalbe‐Koda, D., Akkiraju, K., Xie, T., Giordano, L., Yu, Y., Eom, C. J., Lunger, J. R., Zheng, D. J., Rao, R. R., et al. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00466-5 · Cited by 157
tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes
Balcells, D., & Skjelstad, B. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01041 · Cited by 143
Energy, Phonon, and Dynamic Stability Criteria of Two-Dimensional Materials
Malyi, O. I., Sopiha, K. V., & Persson, C. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b01261 · Cited by 138
Synthesis of Co‐Doped MoS 2 Monolayers with Enhanced Valley Splitting
Zhou, J., Lin, J., Sims, H., et al. (2020) · Advanced Materials · doi:10.1002/adma.201906536 · Cited by 138
High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks
Zhao, Y., Al‐Fahdi, M., Hu, M., Siriwardane, E. M. D., Song, Y., Nasiri, A., & Hu, J. (2021) · Advanced Science · doi:10.1002/advs.202100566 · Cited by 148
“The ternary materials in Materials Project database[34] and ICSD[7] were processed as it was done for OQMD samples to create the MP-TC3 and ICSD-TC3 validation datasets.”
MethodInfluentialExfoliation mechanisms of 2D materials and their applications
Islam, M. A., Serles, P., Kumral, B., Demingos, P. G., Qureshi, T., Ashokkumar, M., Puthirath, A. B., Abdullah, M. S. B., Faysal, S. R., Ajayan, P. M., et al. (2022) · Applied Physics Reviews · doi:10.1063/5.0090717 · Cited by 147
Babu, S. S., Mourad, A. I., Harib, K. H., & Vijayavenkataraman, S. (2022) · Virtual and Physical Prototyping · doi:10.1080/17452759.2022.2141653 · Cited by 153
“This led to the establishment of databases covering known and notional systems, including single crystals, metal alloys (Hachmann et al. 2011; Jain et al. 2013; Calderon et al. 2015), organic and inorganic crystals.”
BackgroundPan, X., Wu, Y., Wang, Y., Zhou, G., & Cai, H. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.154249 · Cited by 149
Urban, A., Matts, I. L., Abdellahi, A., & Ceder, G. (2016) · Advanced Energy Materials · doi:10.1002/aenm.201600488 · Cited by 141
Mining Unexplored Chemistries for Phosphors for High-Color-Quality White-Light-Emitting Diodes
Wang, Z., Ha, J., Kim, Y. H., Im, W. B., McKittrick, J. M., & Ong, S. P. (2018) · Joule · doi:10.1016/j.joule.2018.01.015 · Cited by 137
Self-citationAdvances in Density-Functional Calculations for Materials Modeling
Maurer, R. J., Freysoldt, C., Reilly, A. M., Brandenburg, J. G., Hofmann, O. T., Björkman, T., Lebègue∥, S., & Tkatchenko, A. (2019) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070218-010143 · Cited by 140
Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
Hippalgaonkar, K., Li, Q., Wang, X., Fisher, J. S., Kirkpatrick, J., & Buonassisi, T. (2023) · Nature Reviews Materials · doi:10.1038/s41578-022-00513-1 · Cited by 156
Lian, Z., Dattila, F., & López, N. (2024) · Nature Catalysis · doi:10.1038/s41929-024-01132-5 · Cited by 155
“2e 14,58 .”
InfluentialAccelerating materials science with high-throughput computations and machine learning
Ong, S. P. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2019.01.013 · Cited by 141
Self-citationImproving the Accuracy of Atomistic Simulations of the Electrochemical Interface
Sundararaman, R., Vigil‐Fowler, D., & Schwarz, K. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00800 · Cited by 142
Hayashi, Y., Shiomi, J., Morikawa, J., & Yoshida, R. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00906-4 · Cited by 141
“This is due to the following reasons: (1) high costs of data production, (2) the difficulty in creating common data due to the diversity of polymeric materials in terms of structures and processing conditions, and (3) cultural barriers to avoiding information leakage to competitors(2).”
BackgroundZiatdinov, M. A., Ghosh, A., Wong, C. Y., & Kalinin, S. V. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00555-8 · Cited by 156
Court, C. J., & Cole, J. M. (2018) · Scientific Data · doi:10.1038/sdata.2018.111 · Cited by 148
Dissolution enables dolomite crystal growth near ambient conditions
Kim, J., Kimura, Y., Puchala, B., Yamazaki, T., Becker, U., & Sun, W. (2023) · Science · doi:10.1126/science.adi3690 · Cited by 138
Large-Scale Refinement of Metal−Organic Framework Structures Using Density Functional Theory
Nazarian, D., Camp, J. S., Chung, Y. G., Snurr, R. Q., & Sholl, D. S. (2016) · Chemistry of Materials · doi:10.1021/acs.chemmater.6b04226 · Cited by 135
Luo, Y., Cai, S., Hua, X., et al. (2018) · Advanced Energy Materials · doi:10.1002/aenm.201803072 · Cited by 135
Xu, W., Zhao, X., Zhan, F., He, Q., Wang, H., Chen, J., Wang, H., Ren, X., & Chen, L. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.08.039 · Cited by 135
Representations of Materials for Machine Learning
Damewood, J., Karaguesian, J., Lunger, J. R., Tan, A. R., Xie, M., Peng, J., & Gómez‐Bombarelli, R. (2023) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-080921-085947 · Cited by 148
“Indeed, advances in deep learning and the construction of large-scale materials databases [4, 5, 6, 7, 8, 9] have made it possible to learn representations directly from structural data.”
BackgroundData-Driven Discovery of Full-Visible-Spectrum Phosphor
Li, S., Xia, Y., Amachraa, M., Hung, N. T., Wang, Z., Ong, S. P., & Xie, R. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b02505 · Cited by 136
Self-citationA perspective on MXenes: Their synthesis, properties, and recent applications
Papadopoulou, K., Chroneos, A., Parfitt, D., & Christopoulos, S. G. (2020) · Journal of Applied Physics · doi:10.1063/5.0021485 · Cited by 134
Li, Z., Han, Y., & Qiao, Z. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.036801 · Cited by 134
The Harvard organic photovoltaic dataset
Lopez, S. A., Pyzer‐Knapp, E. O., Simm, G. N. C., Lutzow, T., Li, K., Seress, L. R., Hachmann, J., & Aspuru‐Guzik, A. (2016) · Scientific Data · doi:10.1038/sdata.2016.86 · Cited by 131
Perovskite oxides for application in thermochemical air separation and oxygen storage
Vieten, J., Bulfin, B., Call, F., Lange, M., Schmücker, M., Francke, A., Roeb, M., & Sattler, C. (2016) · Journal of Materials Chemistry A · doi:10.1039/c6ta04867f · Cited by 135
Designing interfaces in energy materials applications with first-principles calculations
Butler, K. T., Gautam, G. S., & Canepa, P. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0160-9 · Cited by 139
Computing environments for reproducibility: Capturing the “Whole Tale”
Brinckman, A., Chard, K., Gaffney, N. I., Hategan, M., Jones, M. B., Kowalik, K., Kulasekaran, S., Ludäscher, B., Mecum, B., Nabrzyski, J., et al. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.12.029 · Cited by 151
Moore, G. C., Horton, M. K., Linscott, E., Ganose, A. M., Siron, M., O’Regan, D. D., & Persson, K. A. (2024) · Physical Review Materials · doi:10.1103/physrevmaterials.8.014409 · Cited by 133
“The Materials Project is a web-based database that contains computed information on a vast range of materials, both known and predicted [37].”
BackgroundSelf-citationTE Design Lab: A virtual laboratory for thermoelectric material design
Gorai, P., Gao, D., Ortiz, B. R., Miller, S., Barnett, S. A., Mason, T. O., Lv, Q., Stevanović, V., & Toberer, E. S. (2015) · Computational Materials Science · doi:10.1016/j.commatsci.2015.11.006 · Cited by 131
A fingerprint based metric for measuring similarities of crystalline structures
Zhu, L., Amsler, M., Fuhrer, T., Schaefer, B., Faraji, S., Rostami, S., Ghasemi, S. A., Sadeghi, A., Grauzinyte, M., Wolverton, C., et al. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4940026 · Cited by 128
AELAS: Automatic ELAStic property derivations via high-throughput first-principles computation
Zhang, S. H., & Zhang, R. (2017) · Computer Physics Communications · doi:10.1016/j.cpc.2017.07.020 · Cited by 131
Machine learning for halide perovskite materials
Zhang, L., He, M., & Shao, S. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.105380 · Cited by 137
Liu, S., Kappes, B. B., Amin-Ahmadi, B., Benafan, O., Zhang, X., & Stebner, A. P. (2020) · Applied Materials Today · doi:10.1016/j.apmt.2020.100898 · Cited by 135
Applications of natural language processing and large language models in materials discovery
Jiang, X., Wang, W., Tian, S., Wang, H., Lookman, T., & Su, Y. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01554-0 · Cited by 145
“The development of data bases of materials has also provided additional sources of accessible data 20 – 24 .”
Hoang, K., & Johannes, M. D. (2014) · Journal of Materials Chemistry A · doi:10.1039/c4ta00673a · Cited by 130
Computational Approach for Epitaxial Polymorph Stabilization through Substrate Selection
Ding, H. Q., Dwaraknath, S., Garten, L. M., et al. (2016) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6b01630 · Cited by 130
Self-citationElastic properties, thermal stability, and thermodynamic parameters of MoAlB
Kota, S. S., Agne, M. T., Zapata‐Solvas, E., et al. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.95.144108 · Cited by 130
Accelerating syngas-to-aromatic conversion via spontaneously monodispersed Fe in ZnCr2O4 spinel
Tian, G., Liu, X., Zhang, C., Fan, X., Xiong, H. D., Chen, X., Li, Z., Yan, B., Zhang, L., Wang, N., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33217-9 · Cited by 132
How to validate machine-learned interatomic potentials
Morrow, J. D., Gardner, J. L. A., & Deringer, V. L. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0139611 · Cited by 149
Popova, E., Rodgers, T. M., Gong, X., Cecen, A., Madison, J. D., & Kalidindi, S. R. (2017) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-017-0088-1 · Cited by 132
Cubuk, E. D., Sendek, A. D., & Reed, E. J. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5093220 · Cited by 131
High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials
Clayson, I. G., Hewitt, D., Hutereau, M., Pope, T., & Slater, B. (2020) · Advanced Materials · doi:10.1002/adma.202002780 · Cited by 138
First-principles calculations for topological quantum materials
Xiao, J., & Yan, B. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00292-8 · Cited by 141
Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning
Jang, J., Gu, G. H., Noh, J., Kim, J., & Jung, Y. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c07384 · Cited by 129
Machine learning for materials design and discovery
Vasudevan, R. K., Pilania, G., & Balachandran, P. V. (2021) · Journal of Applied Physics · doi:10.1063/5.0043300 · Cited by 132
Advances in atomistic modeling and understanding of drying shrinkage in cementitious materials
Qomi, M. J. A., Brochard, L., Honório, T., Maruyama, I., & Vandamme, M. (2021) · Cement and Concrete Research · doi:10.1016/j.cemconres.2021.106536 · Cited by 130
Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols
Jiang, B., Gent, W. E., Mohr, F., Das, S., Berliner, M. D., Forsuelo, M. A., Zhao, H., Attia, P. M., Grover, A., Herring, P. K., et al. (2021) · Joule · doi:10.1016/j.joule.2021.10.010 · Cited by 129
Yin, P., Niu, X., Li, S.-B., Chen, K., Zhang, X., Zuo, M. J., Zhang, L., & Liang, H. (2024) · Nature Communications · doi:10.1038/s41467-023-44674-1 · Cited by 152
“The ordered structures were characterized using various imaging methods.”
Olszowska, K., Pang, J., Wrobel, P. S., Zhao, L., Ta, H. Q., Liu, Z., Trzebicka, B., Bachmatiuk, A., & Rümmeli, M. H. (2017) · Synthetic Metals · doi:10.1016/j.synthmet.2017.10.014 · Cited by 129
Vibrational spectroscopy at atomic resolution with electron impact scattering
Venkatraman, K., Levin, B. D. A., March, K., et al. (2019) · Nature Physics · doi:10.1038/s41567-019-0675-5 · Cited by 128
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Oliver, S., Zhao, L., Gormley, A. J., Chapman, R., & Boyer, C. A. (2018) · Macromolecules · doi:10.1021/acs.macromol.8b01864 · Cited by 130
Deep elastic strain engineering of bandgap through machine learning
Shi, Z., Tsymbalov, E., Dao, M., Suresh, S., Shapeev, A. V., & Li, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1818555116 · Cited by 126
Completing the picture through correlative characterization
Burnett, T. L., & Withers, P. J. (2019) · Nature Materials · doi:10.1038/s41563-019-0402-8 · Cited by 128
Creason, T. D., Yangui, A., Roccanova, R., et al. (2019) · Advanced Optical Materials · doi:10.1002/adom.201901338 · Cited by 128
Gagné, O. C., & Hawthorne, F. C. (2020) · IUCrJ · doi:10.1107/s2052252520005928 · Cited by 137
Huang, J., Zhang, L., Wang, H., Zhao, J., Cheng, J., & Weinan, E. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0041849 · Cited by 127
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“[224]) as thermodynamic free energies are less sensitive to the microstructure (unlike mechanical properties).”
Background
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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