Inorganic Syntheses, Vol. 25


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Acta Crystallogr.


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B Struct. Finding New perovskite halides via machine learning. Balachandran, P. A Math. Machine learning bandgaps of double perovskites. Greeley, J. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Hong, W. Descriptors of oxygen-evolution activity for oxides: A statistical evaluation. C , 78—86 Gaultois, M. Data-driven review of thermoelectric materials: performance and resource considerations BT - chemistry of materials.


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Sparks, T. Data mining our way to the next generation of thermoelectrics. Yan, J. Material descriptors for predicting thermoelectric performance. Energy Environ. Seshadri, R. Perspective: Interactive material property databases through aggregation of literature data. APL Mater. Oliynyk, A. High-throughput machine-learning-driven synthesis of full-heusler compounds. Wilmer, C. Large-scale screening of hypothetical metal—organic frameworks. Lin, L. In silico screening of carbon-capture materials. Materials data infrastructure: A case study of the citrination platform to examine data import, storage, and access.

JOM 68 , — Jain, A. Commentary: The materials project: A materials genome approach to accelerating materials innovation. Kirklin, S.

Inorganic Reaction Mechanism - Tricks and Concept - Explained by IITian - Jee Mains, Advanced - NEET

Pyzer-Knapp, E. Learning from the Harvard Clean Energy Project: The use of neural networks to accelerate materials discovery. Hachmann, J. Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry—the Harvard Clean Energy Project. Raccuglia, P. Machine-learning-assisted materials discovery using failed experiments. Nature , 73—76 Isayev, O. Materials cartography: Representing and mining material space using structural and electronic fingerprints.

Ward, L. A general-purpose machine learning framework for predicting properties of inorganic materials. Npj Comput. Sumpter, B. A bridge for accelerating materials by design. Kalinin, S. Big—deep—smart data in imaging for guiding materials design. Computer-assisted synthetic planning: the end of the beginning. Grzybowski, B. Coley, C. Prediction of organic reaction outcomes using machine learning. ACS Cent. Hawizy, L. ChemicalTagger: A tool for semantic text-mining in chemistry. Goodman, J. Computer software review: Reaxys. ChemSpot: a hybrid system for chemical named entity recognition.

Bioinformatics 28 , — Guha, R. The Blue Obelisk-interoperability in chemical informatics. Murray-Rust, P. Chemical markup, XML, and the world wide web. CML schema. Pence, H. Chemspider: An online chemical information resource. Kim, S. PubChem substance and compound databases. Acids Res.

Ley, S. Organic synthesis: March of the machines. Kim, E. Machine-learned and codified synthesis parameters of oxide materials. Data 4, Roweis, S. Nonlinear dimensionality reduction by locally linear embedding. Science , — Kusne, A.

Nanotechnology 26 , Mikolov, T. Efficient estimation of word representations in vector space. Mnih, V. Human-level control through deep reinforcement learning. Nature , — Wu, Z. MoleculeNet: A benchmark for molecularmachine learning. Carrasquilla, J. Machine learning phases of matter. Gilmer, J. Neural message passing for quantum chemistry. Altae-Tran, H. Low Data drug discovery with one-shot learning. Deng J. ImageNet: A large-scale hierarchical image database. Pattern Recognit. Torralba, A. IEEE Trans. Suh, C. The application of principal component analysis to materials science data.

Data Sci. Ghiringhelli, L. Big data of materials science: critical role of the descriptor. Kingma, D. Auto-encoding variational bayes. International Conference on Learning Representations.

Inorganic Syntheses, Volume 37 | Inorganic Chemistry | Chemistry | Subjects | Wiley

Automatic chemical design using variational autoencoders. Preprint at arxiv. Urban, J. Synthesis of single-crystalline barium titanate and strontium titanate. Ye, M. Garden-like perovskite superstructures with enhanced photocatalytic activity. Nanoscale 6 , Zhang, Q. Natl Acad. Puangpetch, T. Synthesis and photocatalytic activity in methyl orange degradation of mesoporous-assembled SrTiO3 nanocrystals prepared by sol-gel method with the aid of structure-directing surfactant.

A Chem. Pavlovic, V. Yang, L. Data-mined similarity function between material compositions. Goodfellow, I. Generative adversarial nets. Neural Inf. Ye, J. Nanoporous anatase TiO 2 mesocrystals: Additive-free synthesis, remarkable crystalline-phase stability, and improved lithium insertion behavior.

Roy, P. TiO2 nanotubes: Synthesis and applications.

Paola, A. Di, Bellardita, M. Brookite, the least known TiO 2 photocatalyst. Catalysts 3 , 36—73 Tomita, K. A water-soluble titanium complex for the selective synthesis of nanocrystalline brookite, rutile, and anatase by a hydrothermal method. Reyes-Coronado, D. Phase-pure TiO 2 nanoparticles: anatase, brookite and rutile.

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Inorganic Synthesis, Vol. 17

Nanotechnology 19 , Yanqing, Z. Hydrothermal preparation and characterization of brookite-type TiO2 nanocrystallites. Pottier, A. Synthesis of brookite TiO 2 nanoparticles by thermolysis of TiCl 4 in strongly acidic aqueous media. Arnal, P. Preparation of anatase, brookite and rutile at low temperature by non-hydrolytic sol—gel methods.

Kitchaev, D. Thermodynamics of phase selection in MnO 2 framework structures through alkali intercalation and hydration. Robinson, D. Photochemical water oxidation by crystalline polymorphs of manganese oxides: Structural requirements for catalysis. Ueno, T. Snoek, J. Practical Bayesian optimization of machine learning algorithms. Distributed representations of words and phrases and their compositionality.

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Chollet, F. Github, Abadi, M. Ong, S. Python Materials Genomics pymatgen : A robust, open-source python library for materials analysis. Pedregosa, F. Scikit-learn: Machine Learning in Python. Taigman, Y. DeepFace: Closing the gap to human-level performance in face verification. In Proc. Zhao, J. Solid State Electron. Zhao, W. Black strontium titanate nanocrystals of enhanced solar absorption for photocatalysis. CrystEngComm 17 , — Download references. We would also like to acknowledge the tireless efforts of Ellen Finnie in the MIT libraries, support from publishers who provided the substantial content required for our analysis, and research input from Gerbrand Ceder, Daniil Kitchaev, Olga Kononova, and Matthew Staib.

We thank Lusann Yang for providing useful Python scripts. All authors wrote and commented on the manuscript and figures. Correspondence to Elsa Olivetti. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Journal of Chemical Information and Modeling All syntheses presented here have been tested. Free Access. Summary PDF Request permissions. PDF References Request permissions.

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Inorganic Syntheses, Vol. 25
Inorganic Syntheses, Vol. 25
Inorganic Syntheses, Vol. 25
Inorganic Syntheses, Vol. 25
Inorganic Syntheses, Vol. 25
Inorganic Syntheses, Vol. 25
Inorganic Syntheses, Vol. 25
Inorganic Syntheses, Vol. 25
Inorganic Syntheses, Vol. 25

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