Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328137364> ?p ?o ?g. }
- W4328137364 abstract "On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys. In this paper, we present a machine learning framework, namely PREDICT (PRedict properties from Existing Database In Complex alloys Territory), that opens a pathway to predict elastic constants in large compositional space with little computational expense. The framework only relies on the DFT database of binary alloys and predicts Voigt–Reuss–Hill Young’s modulus, shear modulus, bulk modulus, elastic constants, and Poisson’s ratio in MPEAs. We show that the key descriptors of elastic constants are the A–B bond length and cohesive energy. The framework can predict elastic constants in hypothetical compositions as long as the constituent elements are present in the database, thereby enabling property exploration in multi-compositional systems. We illustrate predictions in a FCC Ni-Cu-Au-Pd-Pt system." @default.
- W4328137364 created "2023-03-22" @default.
- W4328137364 creator A5012603904 @default.
- W4328137364 creator A5023250729 @default.
- W4328137364 date "2023-03-01" @default.
- W4328137364 modified "2023-10-06" @default.
- W4328137364 title "A machine learning framework for elastic constants predictions in multi-principal element alloys" @default.
- W4328137364 cites W1973498385 @default.
- W4328137364 cites W1984003326 @default.
- W4328137364 cites W2038445838 @default.
- W4328137364 cites W2047968138 @default.
- W4328137364 cites W2053526307 @default.
- W4328137364 cites W2058705304 @default.
- W4328137364 cites W2077137667 @default.
- W4328137364 cites W2089212231 @default.
- W4328137364 cites W2099073491 @default.
- W4328137364 cites W2181347531 @default.
- W4328137364 cites W2288102383 @default.
- W4328137364 cites W2526943155 @default.
- W4328137364 cites W2527749992 @default.
- W4328137364 cites W2534691303 @default.
- W4328137364 cites W2606735347 @default.
- W4328137364 cites W2750556263 @default.
- W4328137364 cites W2797140242 @default.
- W4328137364 cites W2808589704 @default.
- W4328137364 cites W2883578585 @default.
- W4328137364 cites W2886680171 @default.
- W4328137364 cites W2896041006 @default.
- W4328137364 cites W2897405754 @default.
- W4328137364 cites W2903448849 @default.
- W4328137364 cites W2905677664 @default.
- W4328137364 cites W2912879714 @default.
- W4328137364 cites W2926476802 @default.
- W4328137364 cites W2972418846 @default.
- W4328137364 cites W2974424282 @default.
- W4328137364 cites W2997591727 @default.
- W4328137364 cites W3026094172 @default.
- W4328137364 cites W3037638035 @default.
- W4328137364 cites W3048439699 @default.
- W4328137364 cites W3083787681 @default.
- W4328137364 cites W3100667032 @default.
- W4328137364 cites W3102933769 @default.
- W4328137364 cites W3172720573 @default.
- W4328137364 cites W3180309787 @default.
- W4328137364 cites W3193329485 @default.
- W4328137364 cites W4224129425 @default.
- W4328137364 cites W4224293997 @default.
- W4328137364 cites W4285098234 @default.
- W4328137364 doi "https://doi.org/10.1063/5.0129928" @default.
- W4328137364 hasPublicationYear "2023" @default.
- W4328137364 type Work @default.
- W4328137364 citedByCount "1" @default.
- W4328137364 crossrefType "journal-article" @default.
- W4328137364 hasAuthorship W4328137364A5012603904 @default.
- W4328137364 hasAuthorship W4328137364A5023250729 @default.
- W4328137364 hasBestOaLocation W43281373641 @default.
- W4328137364 hasConcept C121332964 @default.
- W4328137364 hasConcept C121864883 @default.
- W4328137364 hasConcept C147597530 @default.
- W4328137364 hasConcept C152365726 @default.
- W4328137364 hasConcept C159985019 @default.
- W4328137364 hasConcept C185592680 @default.
- W4328137364 hasConcept C192562407 @default.
- W4328137364 hasConcept C33923547 @default.
- W4328137364 hasConcept C41008148 @default.
- W4328137364 hasConcept C41279357 @default.
- W4328137364 hasConcept C43486711 @default.
- W4328137364 hasConcept C48372109 @default.
- W4328137364 hasConcept C94375191 @default.
- W4328137364 hasConcept C94406020 @default.
- W4328137364 hasConcept C97355855 @default.
- W4328137364 hasConceptScore W4328137364C121332964 @default.
- W4328137364 hasConceptScore W4328137364C121864883 @default.
- W4328137364 hasConceptScore W4328137364C147597530 @default.
- W4328137364 hasConceptScore W4328137364C152365726 @default.
- W4328137364 hasConceptScore W4328137364C159985019 @default.
- W4328137364 hasConceptScore W4328137364C185592680 @default.
- W4328137364 hasConceptScore W4328137364C192562407 @default.
- W4328137364 hasConceptScore W4328137364C33923547 @default.
- W4328137364 hasConceptScore W4328137364C41008148 @default.
- W4328137364 hasConceptScore W4328137364C41279357 @default.
- W4328137364 hasConceptScore W4328137364C43486711 @default.
- W4328137364 hasConceptScore W4328137364C48372109 @default.
- W4328137364 hasConceptScore W4328137364C94375191 @default.
- W4328137364 hasConceptScore W4328137364C94406020 @default.
- W4328137364 hasConceptScore W4328137364C97355855 @default.
- W4328137364 hasFunder F4320337480 @default.
- W4328137364 hasIssue "1" @default.
- W4328137364 hasLocation W43281373641 @default.
- W4328137364 hasLocation W43281373642 @default.
- W4328137364 hasOpenAccess W4328137364 @default.
- W4328137364 hasPrimaryLocation W43281373641 @default.
- W4328137364 hasRelatedWork W2325425200 @default.
- W4328137364 hasRelatedWork W2349333354 @default.
- W4328137364 hasRelatedWork W2523163970 @default.
- W4328137364 hasRelatedWork W2621327717 @default.
- W4328137364 hasRelatedWork W2806870877 @default.
- W4328137364 hasRelatedWork W3082431407 @default.
- W4328137364 hasRelatedWork W3091342329 @default.
- W4328137364 hasRelatedWork W4281907618 @default.