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Principal component analysis and dimensional analysis as materials informatics tools to reduce dimensionality in materials science and engineering

https://doi.org/10.1002/sam.10031
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9780470140529openalex-snapshot
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inferredcites →https://doi.org/10.1002/sam.10022openalex-snapshot
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inferredcites →https://doi.org/10.1007/978-3-662-05802-2openalex-snapshot
Applied Multivariate Statistical Analysis
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Materials informatics: Growing from the Bio World
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The materials informatics workshop: Theory and application
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Scaling laws as a tool of materials informatics
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A statistical procedure for model building in dimensional analysis
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Improving the statistical accuracy of dimensional analysis correlations for precise coefficient estimation and optimal design of experiments
inferredcites →https://doi.org/10.1016/b978-0-12-553990-6.x5001-6openalex-snapshot
Physics of High-Tc Superconductors
inferredcites →https://doi.org/10.1016/b978-0-12-561460-3.x5000-0openalex-snapshot
Handbook of Superconductivity
inferredcites →https://doi.org/10.1016/j.msea.2006.06.100openalex-snapshot
Predicting materials properties and behavior using classification and regression trees
inferredcites →https://doi.org/10.1016/j.scriptamat.2005.09.051openalex-snapshot
Metallic glasses as structural materials
inferredcites →https://doi.org/10.1016/s0921-4534(02)01208-xopenalex-snapshot
Study of binary and pseudo-binary intermetallic compounds with AlB2 structure
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Superconductivity in ternary germanide Y(Pt0.5Ge1.5) with the AlB2-type structure
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Bioinspired antifouling polymers
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Selected Concepts and Investigations in Compound Classification, Molecular Descriptor Analysis, and Virtual Screening
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On Physically Similar Systems; Illustrations of the Use of Dimensional Equations
inferredcites →https://doi.org/10.1103/physrevb.37.2345openalex-snapshot
Quantum structural diagrams and high-T_{c} superconductivity
inferredcites →https://doi.org/10.1103/physrevb.63.045115openalex-snapshot
Electronic structure, bonding, and ground-state properties of AlB_{2}-type transition-metal diborides
inferredcites →https://doi.org/10.1103/physrevb.63.172407openalex-snapshot
Observation of enhanced magnetic transition temperature in Nd2PdGe3 and superconductivity in Y2PdGe3
inferredcites →https://doi.org/10.1103/physrevb.64.020502openalex-snapshot
Electronic structure of superconducting MgB2 and related binary and ternary borides
inferredcites →https://doi.org/10.1103/physrevd.4.345openalex-snapshot
Radiation Reaction as a Retarded Self-Interaction
inferredcites →https://doi.org/10.1103/physrevlett.86.5771openalex-snapshot
Phonon Dispersion and Electron-Phonon Coupling in MgB2 and AlB2
inferredcites →https://doi.org/10.1103/physrevlett.87.077003openalex-snapshot
Superconductivity of Ternary Silicide with the AlB2 -Type Structure Sr(Ga0.37,Si0.63)2
inferredcites →https://doi.org/10.1103/physrevlett.87.087004openalex-snapshot
Role of Boronp-Electrons and Holes in Superconducting MgB2 , and Other Diborides: A Fully Relaxed, Full-Potential Electronic Structure Study
inferredcites →https://doi.org/10.1115/1.1943434openalex-snapshot
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inferredcites →https://doi.org/10.1142/9789814343046_0003openalex-snapshot
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inferredcites →https://doi.org/10.1557/proc-804-jj9.23openalex-snapshot
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inferredcites →https://doi.org/10.17877/de290r-10727openalex-snapshot
Role of Boron p-Electrons and Holes in Superconducting MgB2, and Other Diborides
inferredcites →https://doi.org/10.2307/2369148openalex-snapshot
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inferredcites →https://doi.org/10.5860/choice.26-6257openalex-snapshot
Copper oxide superconductors

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inferred← citeshttps://doi.org/10.1016/j.actamat.2015.02.045openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.addma.2020.101641openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.biomaterials.2009.05.069openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/2193-9772-3-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/preaccept-5129428151148844openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s40192-014-0017-5openalex-snapshot
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