Virtual screening of inorganic materials synthesis parameters with deep learning
Open accessAbstract
Virtual materials screening approaches have proliferated in the past decade, driven by rapid advances in first-principles computational techniques, and machine-learning algorithms. By comparison, computationally driven materials synthesis screening is still in its infancy, and is mired by the challenges of data sparsity and data scarcity : Synthesis routes exist in a sparse, high-dimensional parameter space that is difficult to optimize over directly, and, for some materials of interest, only scarce volumes of literature-reported syntheses are available. In this article, we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes. We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space, which is found to improve the performance of machine-learning tasks. To realize this screening framework even in cases where there are few literature data, we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems. We apply this variational autoencoder framework to generate potential SrTiO 3 synthesis parameter sets, propose driving factors for brookite TiO 2 formation, and identify correlations between alkali-ion intercalation and MnO 2 polymorph selection.
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965 · see citing works
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 · doi:10.1063/1.4812323 · Cited by 13,094 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691 · see citing works
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542 · see citing works
PubChem Substance and Compound databases
Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436 · see citing works
Neural Message Passing for Quantum ChemistryPreprint
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,414 · see citing works
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. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,339 · see citing works
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 · see citing works
TiO 2 Nanotubes: Synthesis and Applications
Roy, P., Berger, S., & Schmuki, P. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201001374 · Cited by 3,135 · see citing works
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107 · see citing works
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 2,385 · see citing works
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
Torralba, A., Fergus, R., & Freeman, W. T. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.128 · Cited by 1,921 · see citing works
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 · see citing works
Machine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727 · see citing works
Machine learning phases of matter
Carrasquilla, J., & Melko, R. G. (2017) · Nature Physics · doi:10.1038/nphys4035 · Cited by 1,448 · see citing works
Large-scale screening of hypothetical metal–organic frameworks
Wilmer, C. E., Leaf, M., Lee, C. Y., Farha, O. K., Hauser, B. G., Hupp, J. T., & Snurr, R. Q. (2012) · Nature Chemistry · doi:10.1038/nchem.1192 · Cited by 1,439 · see citing works
ChemSpider: An Online Chemical Information Resource
Pence, H. E., & Williams, A. J. (2010) · Journal of Chemical Education · doi:10.1021/ed100697w · Cited by 1,277 · see citing works
Phase-pure TiO 2 nanoparticles: anatase, brookite and rutile
Coronado, D. R., Rodríguez‐Gattorno, G., Espinosa-Pesqueira, M. E., Cab, C., de Coss, R., & Oskam, G. (2008) · Nanotechnology · doi:10.1088/0957-4484/19/14/145605 · Cited by 1,171 · see citing works
Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., Hirzel, T. D., Duvenaud, D., Maclaurin, D., Blood-Forsythe, M. A., Chae, H. S., Einzinger, M., Ha, D.-G., Wu, T., et al. (2016) · Nature Materials · doi:10.1038/nmat4717 · Cited by 1,060 · see citing works
Big Data of Materials Science: Critical Role of the Descriptor
Ghiringhelli, L. M., Vybiral, J., Levchenko, S. V., Draxl, C., & Scheffler, M. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.105503 · Cited by 920 · see citing works
Low Data Drug Discovery with One-Shot Learning
Altae-Tran, H., Ramsundar, B., Pappu, A. S., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.6b00367 · Cited by 799 · see citing works
Prediction of Organic Reaction Outcomes Using Machine Learning
Coley, C. W., Barzilay, R., Jaakkola, T., Green, W. H., & Jensen, K. F. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00064 · Cited by 755 · see citing works
Robinson, D. M., Go, Y. B., Mui, M., Gardner, G., Zhang, Z., Mastrogiovanni, D. D. T., Garfunkel, E. L., Li, J., Greenblatt, M., & Dismukes, G. C. (2013) · Journal of the American Chemical Society · doi:10.1021/ja310286h · Cited by 639 · see citing works
Brookite, the Least Known TiO2 Photocatalyst
Paola, A. D., Bellardita, M., & Palmisano, L. (2013) · Catalysts · doi:10.3390/catal3010036 · Cited by 628 · see citing works
Ye, J., Liu, W., Cai, J., Chen, S., Zhao, X., Zhou, H., & Qi, L. (2010) · Journal of the American Chemical Society · doi:10.1021/ja108205q · Cited by 626 · see citing works
Computer‐Assisted Synthetic Planning: The End of the Beginning
Szymkuć, S., Gajewska, E. P., Klucznik, T., Molga, K., Dittwald, P., Startek, M., Bajczyk, M., & Grzybowski, B. A. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201506101 · Cited by 611 · see citing works
In silico screening of carbon-capture materials
Lin, L., Berger, A. H., Martin, R. L., Kim, J., Swisher, J. A., Jariwala, K., Rycroft, C. H., Bhown, A. S., Deem, M. W., Harańczyk, M., et al. (2012) · Nature Materials · doi:10.1038/nmat3336 · Cited by 564 · see citing works
Machine learning bandgaps of double perovskites
Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., & Lookman, T. (2016) · Scientific Reports · doi:10.1038/srep19375 · Cited by 518 · see citing works
Data-Driven Review of Thermoelectric Materials: Performance and Resource Considerations
Gaultois, M. W., Sparks, T. D., Borg, C. K. H., Seshadri, R., Bonificio, W. D., & Clarke, D. R. (2013) · Chemistry of Materials · doi:10.1021/cm400893e · Cited by 503 · see citing works
Organic Synthesis: March of the Machines
Ley, S. V., Fitzpatrick, D. E., Ingham, R. J., & Myers, R. M. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201410744 · Cited by 455 · see citing works
Urban, J. J., Yun, W. S., Gu, Q. J., & Park, H. (2002) · Journal of the American Chemical Society · doi:10.1021/ja017694b · Cited by 446 · see citing works
Synthesis of brookite TiO2 nanoparticles by thermolysis of TiCl4 in strongly acidic aqueous media
Pottier, A., Chanéac, C., Tronc, É., et al. (2001) · Journal of Materials Chemistry · doi:10.1039/b100435m · Cited by 402 · see citing works
The Blue Obelisk—Interoperability in Chemical Informatics
Guha, R., Howard, M. T., Hutchison, G., Murray‐Rust, P., Rzepa, H. S., Steinbeck, C., Wegner, J. K., & Willighagen, E. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050400b · Cited by 390 · see citing works
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
Oliynyk, A. O., Antono, E., Sparks, T. D., Ghadbeigi, L., Gaultois, M. W., Meredig, B., & Mar, A. (2016) · Chemistry of Materials · doi:10.1021/acs.chemmater.6b02724 · Cited by 387 · see citing works
Big–deep–smart data in imaging for guiding materials design
Kalinin, S. V., Sumpter, B. G., & Archibald, R. K. (2015) · Nature Materials · doi:10.1038/nmat4395 · Cited by 347 · see citing works
Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation
Hong, W. T., Welsch, R. E., & Shao‐Horn, Y. (2015) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.5b10071 · Cited by 316 · see citing works
COMBO: An efficient Bayesian optimization library for materials science
Ueno, T., Rhone, T. D., Hou, Z., Mizoguchi, T., & Tsuda, K. (2016) · Materials Discovery · doi:10.1016/j.md.2016.04.001 · Cited by 309 · see citing works
Isayev, O., Fourches, D., Muratov, E. N., Oses, C., Rasch, K., Tropsha, A., & Curtarolo, S. (2015) · Chemistry of Materials · doi:10.1021/cm503507h · Cited by 300 · see citing works
Material descriptors for predicting thermoelectric performance
Yan, J., Gorai, P., Ortiz, B. R., Miller, S., Barnett, S. A., Mason, T. O., Stevanović, V., & Toberer, E. S. (2014) · Energy & Environmental Science · doi:10.1039/c4ee03157a · Cited by 283 · see citing works
ChemSpot: a hybrid system for chemical named entity recognition
Rocktäschel, T., Weidlich, M., & Leser, U. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts183 · Cited by 269 · see citing works
Tomita, K., Petrykin, V., Kobayashi, M., et al. (2006) · Angewandte Chemie International Edition · doi:10.1002/anie.200503565 · Cited by 254 · see citing works
Hachmann, J., Olivares‐Amaya, R., Jinich, A., Appleton, A. L., Blood-Forsythe, M. A., Seress, L. R., Román-Salgado, C., Trepte, K., Atahan-Evrenk, Ş., Er, S., et al. (2013) · Energy & Environmental Science · doi:10.1039/c3ee42756k · Cited by 247 · see citing works
Kitchaev, D. A., Dacek, S. T., Sun, W. Q., et al. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.6b11301 · Cited by 232 · see citing works
Pyzer‐Knapp, E. O., Li, K., & Aspuru‐Guzik, A. (2015) · Advanced Functional Materials · doi:10.1002/adfm.201501919 · Cited by 223 · see citing works
Finding New Perovskite Halides via Machine Learning
Pilania, G., Balachandran, P. V., Kim, C., & Lookman, T. (2016) · Frontiers in Materials · doi:10.3389/fmats.2016.00019 · Cited by 206 · see citing works
ChemicalTagger: A tool for semantic text-mining in chemistry
Hawizy, L., Jessop, D. M., Adams, N., & Murray‐Rust, P. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-17 · Cited by 192 · see citing works
Chemical Markup, XML, and the World Wide Web. 4. CML Schema
Murray‐Rust, P., & Rzepa, H. S. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0256541 · Cited by 174 · see citing works
The 'wired' universe of organic chemistry
Grzybowski, B. A., Bishop, K. J. M., Kowalczyk, B., et al. (2009) · Nature Chemistry · doi:10.1038/nchem.136 · Cited by 166 · see citing works
Machine-learned and codified synthesis parameters of oxide materials
Kim, E., Huang, K., Tomala, A., Matthews, S., Strubell, E., Saunders, A. M., McCallum, A., & Olivetti, E. (2017) · Scientific Data · doi:10.1038/sdata.2017.127 · Cited by 165 · see citing works
Self-citationThe ferroelectric and cubic phases in BaTiO 3 ferroelectrics are also antiferroelectric
Zhang, Q., Çağın, T., & Goddard, W. A. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606612103 · Cited by 152 · see citing works
Puangpetch, T., Sreethawong, T., Yoshikawa, S., et al. (2008) · Journal of Molecular Catalysis A Chemical · doi:10.1016/j.molcata.2008.02.027 · Cited by 141 · see citing works
Preparation of anatase, brookite and rutile at low temperature by non-hydrolytic sol–gel methods
Arnal, P., Corriu, R. J. P., Leclercq, D., et al. (1996) · Journal of Materials Chemistry · doi:10.1039/jm9960601925 · Cited by 141 · see citing works
Computer Software Review: Reaxys
Goodman, J. M. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900437n · Cited by 131 · see citing works
Data mining our way to the next generation of thermoelectrics
Sparks, T. D., Gaultois, M. W., Oliynyk, A. O., et al. (2015) · Scripta Materialia · doi:10.1016/j.scriptamat.2015.04.026 · Cited by 126 · see citing works
O’Mara, J., Meredig, B., & Michel, K. J. (2016) · JOM · doi:10.1007/s11837-016-1984-0 · Cited by 110 · see citing works
Balachandran, P. V., Broderick, S., & Rajan, K. (2011) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2010.0543 · Cited by 98 · see citing works
Hydrothermal preparation and characterization of brookite-type TiO2 nanocrystallites
Yanqing, Z., Er-wei, S., Suxian, C., et al. (2000) · Journal of Materials Science Letters · doi:10.1023/a:1011010306699 · Cited by 87 · see citing works
High-throughput determination of structural phase diagram and constituent phases using GRENDEL
Kusne, A. G., Keller, D. A., Anderson, A. Y., Zaban, A., & Takeuchi, I. (2015) · Nanotechnology · doi:10.1088/0957-4484/26/44/444002 · Cited by 77 · see citing works
Carbon Materials as Additives to WO3 for an Enhanced Conversion of Simulated Solar Light
Carmona, R. J., Velasco, L. F., Laurenti, E., Maurino, V., & Ania, C. O. (2016) · Frontiers in Materials · doi:10.3389/fmats.2016.00009 · Cited by 73 · see citing works
Classification ofABO3perovskite solids: a machine learning study
Pilania, G., Balachandran, P. V., Gubernatis, J. E., & Lookman, T. (2015) · Acta Crystallographica Section B Structural Science Crystal Engineering and Materials · doi:10.1107/s2052520615013979 · Cited by 72 · see citing works
The application of Principal Component Analysis to materials science data
Suh, C., Rajagopalan, A., Li, X., et al. (2002) · Data Science Journal · doi:10.2481/dsj.1.19 · Cited by 65 · see citing works
Garden-like perovskite superstructures with enhanced photocatalytic activity
Ye, M., Wang, M., Zheng, D., et al. (2013) · Nanoscale · doi:10.1039/c3nr05564g · Cited by 60 · see citing works
A bridge for accelerating materials by design
Sumpter, B. G., Vasudevan, R. K., Potok, T. E., & Kalinin, S. V. (2015) · npj Computational Materials · doi:10.1038/npjcompumats.2015.8 · Cited by 56 · see citing works
MoleculeNet: A Benchmark for Molecular Machine LearningPreprint
Wu, Z., Ramsundar, B., Feinberg, E. N., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00564 · Cited by 51 · see citing works
Black strontium titanate nanocrystals of enhanced solar absorption for photocatalysis
Zhao, W., Zhao, W., Zhu, G., et al. (2015) · CrystEngComm · doi:10.1039/c5ce01263e · Cited by 49 · see citing works
Perspective: Interactive material property databases through aggregation of literature data
Seshadri, R., & Sparks, T. D. (2016) · APL Materials · doi:10.1063/1.4944682 · Cited by 47 · see citing works
Data-mined similarity function between material compositions
Yang, L., & Ceder, G. (2013) · Physical Review B · doi:10.1103/physrevb.88.224107 · Cited by 32 · see citing works
Synthesis of BaTiO3 from a mechanically activated BaCO3-TiO2 system
Pavlović, V. P., Stojanović, B. D., Pavlović, V. B., et al. (2008) · Science of Sintering · doi:10.2298/sos0801021p · Cited by 28 · see citing works
Preparation and electrical properties of SrTiO3 ceramics doped with M2O3–PbO–CuO
Zhao, J., Wu, X., Li, L., et al. (2004) · Solid-State Electronics · doi:10.1016/j.sse.2004.04.009 · Cited by 25 · see citing works
BiS 2 ベース超伝導体の三重ペアリングと可能性のある弱いトポロジカル超伝導性
Yang, Y., Wang, W., Xiang, Y., et al. (2013) · Physical Review B · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Torralba, A., Fergus, R. & Freeman, W. T. 80 Millions tiny images: a large dataset for non-parametric object and scene recognition. IEEE Trans. Pattern. Anal. Mach. Intell. 30, 1958-1970 (2008).
- Gómez-Bombarelli, R., Hirzel, T. D., Duvenaud, D., Aguilera-Iparraguirre, J. & Adams, R. P. Automatic chemical design using variational autoencoders. ArXiv. Preprint at arxiv.org/abs/1610.02415 (2017). · arXiv
- Goodfellow, I. et al. Generative adversarial nets. Adv. Neural Inf. Process. Syst. (2014).
Cited by
Sorted by citations
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 · doi:10.1038/s41524-022-00734-6 · Cited by 1,172 · see citing works
“Such data has been used to predict synthesis maps for titania nanotubes [310], various binary and ternary oxides [318], and perovskites [319].”
MethodMater, A. C., & Coote, M. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00266 · Cited by 565 · see citing works
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446 · see citing works
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works
Data-driven materials research enabled by natural language processing and information extraction
Olivetti, E. A., Cole, J. M., Kim, E., Kononova, O., Ceder, G., Han, T. Y.-J., & Hiszpanski, A. M. (2020) · Applied Physics Reviews · doi:10.1063/5.0021106 · Cited by 302 · see citing works
Self-citationNandy, A., Duan, C., & Kulik, H. J. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c07217 · Cited by 200 · see citing works
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 · see citing works
Self-citationSemi-supervised machine-learning classification of materials synthesis procedures
Huo, H., Rong, Z., Kononova, O., Sun, W., Botari, T., He, T., Tshitoyan, V., & Ceder, G. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0204-1 · Cited by 151 · see citing works
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 · see citing works
Machine Learning for the Discovery, Design, and Engineering of Materials.
Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works
Ravinder, Venugopal, V., Bishnoi, S., Singh, S., Zaki, M., Grover, H. S., Bauchy, M., Agarwal, M., & Krishnan, N. M. A. (2021) · International Journal of Applied Glass Science · doi:10.1111/ijag.15881 · Cited by 56 · see citing works
- Authors
- Kim, E., Huang, K., Jegelka, S., & Olivetti, E.
- Affiliations
- Massachusetts Institute of Technology (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Corresponding author
- Olivetti, Elsa
- Venue
- npj Computational Materials
- Published
- 2017-12-01
- Volume
- 3
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W2770164889
- Cited by
- 190
- References
- 78
- Citations by year
- 2026: 6 · 2025: 19 · 2024: 21 · 2023: 15 · 2022: 28 · 2021: 35 · 2020: 28 · 2019: 32 · 2018: 6
- FWCI
- 7.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Computational Materials (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Science Foundation (1534340, 1553284, CAREER)
- U.S. Department of Energy
- Natural Sciences and Engineering Research Council of Canada
- Office of Naval Research (N00014-16-1-2432, N00014)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- inorganic synthesis, synthesis condition optimization, variational autoencoder, data augmentation, data sparsity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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