FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204184032> ?p ?o ?g. }

• W3204184032 endingPage "104070" @default.
• W3204184032 startingPage "104070" @default.
• W3204184032 abstract "We propose an approach for exploiting machine learning to approximate electronic fields in crystalline solids subjected to deformation. Strain engineering is emerging as a widely used method for tuning the properties of materials, and this requires repeated density functional theory calculations of the unit cell subjected to strain. Repeated unit cell calculations are also required for multi-resolution studies of defects in crystalline solids. We propose an approach that uses data from such calculations to train a carefully architected machine learning approximation. We demonstrate the approach on magnesium, a promising light-weight structural material: we show that we can predict the energy and electronic fields to the level of chemical accuracy, and even capture lattice instabilities." @default.
• W3204184032 created "2021-10-11" @default.
• W3204184032 creator A5024960360 @default.
• W3204184032 creator A5027535616 @default.
• W3204184032 creator A5052584123 @default.
• W3204184032 date "2021-12-01" @default.
• W3204184032 modified "2023-10-11" @default.
• W3204184032 title "Machine-learned prediction of the electronic fields in a crystal" @default.
• W3204184032 cites W1911433256 @default.
• W3204184032 cites W1972395371 @default.
• W3204184032 cites W1973663039 @default.
• W3204184032 cites W1978538703 @default.
• W3204184032 cites W1987577630 @default.
• W3204184032 cites W2010806444 @default.
• W3204184032 cites W2014362516 @default.
• W3204184032 cites W2016007037 @default.
• W3204184032 cites W2031881616 @default.
• W3204184032 cites W2044697870 @default.
• W3204184032 cites W2066911549 @default.
• W3204184032 cites W2068048050 @default.
• W3204184032 cites W2076605490 @default.
• W3204184032 cites W2076770459 @default.
• W3204184032 cites W2078321088 @default.
• W3204184032 cites W2098432400 @default.
• W3204184032 cites W2109939584 @default.
• W3204184032 cites W2111124526 @default.
• W3204184032 cites W2126442689 @default.
• W3204184032 cites W2134497066 @default.
• W3204184032 cites W2140884041 @default.
• W3204184032 cites W2141519533 @default.
• W3204184032 cites W2194775991 @default.
• W3204184032 cites W2230728100 @default.
• W3204184032 cites W2337437141 @default.
• W3204184032 cites W2338997678 @default.
• W3204184032 cites W2398780742 @default.
• W3204184032 cites W2514466325 @default.
• W3204184032 cites W2578764219 @default.
• W3204184032 cites W2624679515 @default.
• W3204184032 cites W2810729069 @default.
• W3204184032 cites W2889703828 @default.
• W3204184032 cites W2921533983 @default.
• W3204184032 cites W2923347852 @default.
• W3204184032 cites W2954442695 @default.
• W3204184032 cites W2983465331 @default.
• W3204184032 cites W2984866711 @default.
• W3204184032 cites W3002798161 @default.
• W3204184032 cites W3043479795 @default.
• W3204184032 cites W3046877548 @default.
• W3204184032 cites W3048497553 @default.
• W3204184032 cites W3099950071 @default.
• W3204184032 cites W3178968719 @default.
• W3204184032 cites W4211105458 @default.
• W3204184032 doi "https://doi.org/10.1016/j.mechmat.2021.104070" @default.
• W3204184032 hasPublicationYear "2021" @default.
• W3204184032 type Work @default.
• W3204184032 sameAs 3204184032 @default.
• W3204184032 citedByCount "6" @default.
• W3204184032 countsByYear W32041840322021 @default.
• W3204184032 countsByYear W32041840322022 @default.
• W3204184032 crossrefType "journal-article" @default.
• W3204184032 hasAuthorship W3204184032A5024960360 @default.
• W3204184032 hasAuthorship W3204184032A5027535616 @default.
• W3204184032 hasAuthorship W3204184032A5052584123 @default.
• W3204184032 hasBestOaLocation W32041840321 @default.
• W3204184032 hasConcept C115624301 @default.
• W3204184032 hasConcept C119857082 @default.
• W3204184032 hasConcept C121332964 @default.
• W3204184032 hasConcept C121864883 @default.
• W3204184032 hasConcept C127413603 @default.
• W3204184032 hasConcept C154945302 @default.
• W3204184032 hasConcept C159985019 @default.
• W3204184032 hasConcept C185592680 @default.
• W3204184032 hasConcept C192562407 @default.
• W3204184032 hasConcept C204366326 @default.
• W3204184032 hasConcept C24890656 @default.
• W3204184032 hasConcept C26873012 @default.
• W3204184032 hasConcept C2781204021 @default.
• W3204184032 hasConcept C41008148 @default.
• W3204184032 hasConcept C78519656 @default.
• W3204184032 hasConcept C8010536 @default.
• W3204184032 hasConcept C84947059 @default.
• W3204184032 hasConcept C86025842 @default.
• W3204184032 hasConceptScore W3204184032C115624301 @default.
• W3204184032 hasConceptScore W3204184032C119857082 @default.
• W3204184032 hasConceptScore W3204184032C121332964 @default.
• W3204184032 hasConceptScore W3204184032C121864883 @default.
• W3204184032 hasConceptScore W3204184032C127413603 @default.
• W3204184032 hasConceptScore W3204184032C154945302 @default.
• W3204184032 hasConceptScore W3204184032C159985019 @default.
• W3204184032 hasConceptScore W3204184032C185592680 @default.
• W3204184032 hasConceptScore W3204184032C192562407 @default.
• W3204184032 hasConceptScore W3204184032C204366326 @default.
• W3204184032 hasConceptScore W3204184032C24890656 @default.
• W3204184032 hasConceptScore W3204184032C26873012 @default.
• W3204184032 hasConceptScore W3204184032C2781204021 @default.
• W3204184032 hasConceptScore W3204184032C41008148 @default.
• W3204184032 hasConceptScore W3204184032C78519656 @default.
• W3204184032 hasConceptScore W3204184032C8010536 @default.

• next