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Mining Materials Design Rules from Data: The Example of Polymer Dielectrics
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9781119148739.ch4openalex-snapshot
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inferredcites →https://doi.org/10.1002/adma.201404162openalex-snapshot
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inferredcites →https://doi.org/10.1002/adma.201600377openalex-snapshot
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inferredcites →https://doi.org/10.1002/qua.24836openalex-snapshot
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inferredcites →https://doi.org/10.1002/qua.24917openalex-snapshot
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inferredcites →https://doi.org/10.1002/qua.24939openalex-snapshot
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inferredcites →https://doi.org/10.1002/qua.25040openalex-snapshot
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inferredcites →https://doi.org/10.1007/978-1-4020-6291-9openalex-snapshot
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inferredcites →https://doi.org/10.1007/s10853-014-8640-2openalex-snapshot
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inferredcites →https://doi.org/10.1007/s11837-013-0755-4openalex-snapshot
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inferredcites →https://doi.org/10.1016/0001-6160(54)90187-xopenalex-snapshot
Atomic theory for students of metallurgy.
inferredcites →https://doi.org/10.1016/0025-5416(87)90343-0openalex-snapshot
Materials science and engineering: An introduction
inferredcites →https://doi.org/10.1016/b978-0-08-054819-7.00001-7openalex-snapshot
Polymer Properties
inferredcites →https://doi.org/10.1016/j.commatsci.2012.02.002openalex-snapshot
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
inferredcites →https://doi.org/10.1016/j.commatsci.2015.11.013openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.commatsci.2016.05.034openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.commatsci.2016.08.018openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.commatsci.2016.08.039openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.commatsci.2016.12.007openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.cossms.2016.07.002openalex-snapshot
Atomistic calculations and materials informatics: A review
inferredcites →https://doi.org/10.1016/j.pmatsci.2016.05.001openalex-snapshot
Advanced polymeric dielectrics for high energy density applications
inferredcites →https://doi.org/10.1016/j.polymer.2013.05.003openalex-snapshot
The rational design of polyurea & polyurethane dielectric materials
inferredcites →https://doi.org/10.1016/j.progpolymsci.2008.08.001openalex-snapshot
Polymer electronic memories: Materials, devices and mechanisms
inferredcites →https://doi.org/10.1021/acs.chemmater.5b04109openalex-snapshot
From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
inferredcites →https://doi.org/10.1021/acs.chemrev.5b00165openalex-snapshot
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inferredcites →https://doi.org/10.1021/acs.jctc.5b00099openalex-snapshot
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inferredcites →https://doi.org/10.1214/ss/1177012580openalex-snapshot
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inferredcites →https://doi.org/10.3389/fmats.2016.00019openalex-snapshot
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