Matches in SemOpenAlex for { <https://semopenalex.org/work/W2894543465> ?p ?o ?g. }
- W2894543465 endingPage "79" @default.
- W2894543465 startingPage "59" @default.
- W2894543465 abstract "In materials informatics, features (or descriptors) that capture trends in the structure, chemistry and/or bonding for a given chemical composition are crucial. Here, we explore their role in the accelerated search for new materials using machine learning adaptive design. We focus on a specific class of materials referred to as apatites [A $$_{10}$$ (BO $$_4$$ ) $$_6$$ X $$_2$$ ] and our objective is to identify an apatite compound with the largest band gap (E $$_g$$ ) without performing density functional theory calculations over the entire composition space. We construct three datasets that use three sets of features of the A, B, and X-ions (ionic radii, electronegativities, and the combination of both) and independently track which of these sets finds most rapidly the composition with the largest E $$_g$$ . We find that the combined feature set performs best, followed by the ionic radii feature set. The reason for this ranking is the B-site ionic radius, which is the key E $$_g$$ -governing feature and appears in both the ionic radii and combined feature sets. Our results show that a relatively poor ML model with large error but one that contains key features can be more efficient in accelerating the search than a low-error model that lack such features." @default.
- W2894543465 created "2018-10-05" @default.
- W2894543465 creator A5026902132 @default.
- W2894543465 creator A5029281241 @default.
- W2894543465 creator A5048145278 @default.
- W2894543465 creator A5062304256 @default.
- W2894543465 creator A5075957872 @default.
- W2894543465 creator A5087470453 @default.
- W2894543465 date "2018-01-01" @default.
- W2894543465 modified "2023-10-15" @default.
- W2894543465 title "Importance of Feature Selection in Machine Learning and Adaptive Design for Materials" @default.
- W2894543465 cites W1510052597 @default.
- W2894543465 cites W1812560530 @default.
- W2894543465 cites W1814328507 @default.
- W2894543465 cites W1983597949 @default.
- W2894543465 cites W2008121308 @default.
- W2894543465 cites W2016168218 @default.
- W2894543465 cites W2025556769 @default.
- W2894543465 cites W2036113194 @default.
- W2894543465 cites W2042358449 @default.
- W2894543465 cites W2051427315 @default.
- W2894543465 cites W2066544127 @default.
- W2894543465 cites W2070059498 @default.
- W2894543465 cites W2087585288 @default.
- W2894543465 cites W2092386656 @default.
- W2894543465 cites W2107436920 @default.
- W2894543465 cites W2112415299 @default.
- W2894543465 cites W2113168655 @default.
- W2894543465 cites W2146462263 @default.
- W2894543465 cites W2150659340 @default.
- W2894543465 cites W2163922914 @default.
- W2894543465 cites W2164524421 @default.
- W2894543465 cites W2167590372 @default.
- W2894543465 cites W2172086781 @default.
- W2894543465 cites W2188358866 @default.
- W2894543465 cites W2230728100 @default.
- W2894543465 cites W2261108203 @default.
- W2894543465 cites W2322354531 @default.
- W2894543465 cites W2335591923 @default.
- W2894543465 cites W2337110853 @default.
- W2894543465 cites W2406005853 @default.
- W2894543465 cites W2437591545 @default.
- W2894543465 cites W2461312660 @default.
- W2894543465 cites W2498941985 @default.
- W2894543465 cites W2529958128 @default.
- W2894543465 cites W2555683692 @default.
- W2894543465 cites W2561003326 @default.
- W2894543465 cites W2565212977 @default.
- W2894543465 cites W2568014457 @default.
- W2894543465 cites W2576881135 @default.
- W2894543465 cites W2588572017 @default.
- W2894543465 doi "https://doi.org/10.1007/978-3-319-99465-9_3" @default.
- W2894543465 hasPublicationYear "2018" @default.
- W2894543465 type Work @default.
- W2894543465 sameAs 2894543465 @default.
- W2894543465 citedByCount "18" @default.
- W2894543465 countsByYear W28945434652019 @default.
- W2894543465 countsByYear W28945434652020 @default.
- W2894543465 countsByYear W28945434652021 @default.
- W2894543465 countsByYear W28945434652022 @default.
- W2894543465 countsByYear W28945434652023 @default.
- W2894543465 crossrefType "book-chapter" @default.
- W2894543465 hasAuthorship W2894543465A5026902132 @default.
- W2894543465 hasAuthorship W2894543465A5029281241 @default.
- W2894543465 hasAuthorship W2894543465A5048145278 @default.
- W2894543465 hasAuthorship W2894543465A5062304256 @default.
- W2894543465 hasAuthorship W2894543465A5075957872 @default.
- W2894543465 hasAuthorship W2894543465A5087470453 @default.
- W2894543465 hasConcept C11413529 @default.
- W2894543465 hasConcept C119857082 @default.
- W2894543465 hasConcept C120665830 @default.
- W2894543465 hasConcept C121332964 @default.
- W2894543465 hasConcept C124101348 @default.
- W2894543465 hasConcept C138885662 @default.
- W2894543465 hasConcept C145148216 @default.
- W2894543465 hasConcept C148483581 @default.
- W2894543465 hasConcept C154945302 @default.
- W2894543465 hasConcept C177264268 @default.
- W2894543465 hasConcept C177293861 @default.
- W2894543465 hasConcept C178635117 @default.
- W2894543465 hasConcept C178790620 @default.
- W2894543465 hasConcept C185592680 @default.
- W2894543465 hasConcept C189430467 @default.
- W2894543465 hasConcept C192209626 @default.
- W2894543465 hasConcept C199360897 @default.
- W2894543465 hasConcept C2182769 @default.
- W2894543465 hasConcept C26517878 @default.
- W2894543465 hasConcept C2776401178 @default.
- W2894543465 hasConcept C38652104 @default.
- W2894543465 hasConcept C41008148 @default.
- W2894543465 hasConcept C41895202 @default.
- W2894543465 hasConcept C54998646 @default.
- W2894543465 hasConceptScore W2894543465C11413529 @default.
- W2894543465 hasConceptScore W2894543465C119857082 @default.
- W2894543465 hasConceptScore W2894543465C120665830 @default.
- W2894543465 hasConceptScore W2894543465C121332964 @default.
- W2894543465 hasConceptScore W2894543465C124101348 @default.
- W2894543465 hasConceptScore W2894543465C138885662 @default.