Matches in SemOpenAlex for { <https://semopenalex.org/work/W3113179618> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W3113179618 endingPage "283" @default.
- W3113179618 startingPage "276" @default.
- W3113179618 abstract "Abstract Ab initio methods have been the workhorse for the computational investigation of new materials during the past few decades. In spite of the improvements regarding the efficiency and scalability achieved by various implementations, the self-consistent solution of the Konhn-Sham equations remains challenging as the size of the system increases. We propose here machine learning methods based on a feature-free deep ANN approach that are able to predict the ground state charge density by starting from readily accessible free-atom charge densities, thus bypassing the usual Hamiltonian diagonalization. We validate our approach on hybrid C-BN nanoflakes with random atomic configurations by comparing the predicted charge density to that computed by DFT. The ANN architecture is optimized in order to reach the high prediction accuracy required to extract ground state based material properties. In order to correlate the effect of spatial rotations in the input-output mapping, we introduce a novel rotational equivariant network (RE-ANN), by properly symmetrizing the synaptic weights during training. This regularization procedure enhances the prediction accuracy, provides consistent results under rotation operations and also increases the sparsity of the weight matrix. These methods have the potential to speed-up DFT simulations and can be used as high throughput investigation tools." @default.
- W3113179618 created "2020-12-21" @default.
- W3113179618 creator A5021033209 @default.
- W3113179618 creator A5064513346 @default.
- W3113179618 date "2021-04-01" @default.
- W3113179618 modified "2023-09-24" @default.
- W3113179618 title "Ground state charge density prediction in C-BN nanoflakes using rotation equivariant feature-free artificial neural networks" @default.
- W3113179618 cites W2026907619 @default.
- W3113179618 cites W2045596260 @default.
- W3113179618 cites W2141704677 @default.
- W3113179618 cites W2742835787 @default.
- W3113179618 cites W2778051509 @default.
- W3113179618 cites W2788254139 @default.
- W3113179618 cites W2800129141 @default.
- W3113179618 cites W2806201317 @default.
- W3113179618 cites W2889703828 @default.
- W3113179618 cites W2904011769 @default.
- W3113179618 cites W2915603306 @default.
- W3113179618 cites W2921533983 @default.
- W3113179618 cites W2956850742 @default.
- W3113179618 cites W2963207368 @default.
- W3113179618 cites W2963731249 @default.
- W3113179618 cites W2968923792 @default.
- W3113179618 cites W2984234582 @default.
- W3113179618 cites W2999990080 @default.
- W3113179618 cites W3006274907 @default.
- W3113179618 cites W3010160899 @default.
- W3113179618 cites W3044485111 @default.
- W3113179618 cites W3046732502 @default.
- W3113179618 cites W3099950071 @default.
- W3113179618 doi "https://doi.org/10.1016/j.carbon.2020.12.048" @default.
- W3113179618 hasPublicationYear "2021" @default.
- W3113179618 type Work @default.
- W3113179618 sameAs 3113179618 @default.
- W3113179618 citedByCount "1" @default.
- W3113179618 countsByYear W31131796182022 @default.
- W3113179618 crossrefType "journal-article" @default.
- W3113179618 hasAuthorship W3113179618A5021033209 @default.
- W3113179618 hasAuthorship W3113179618A5064513346 @default.
- W3113179618 hasConcept C121332964 @default.
- W3113179618 hasConcept C138885662 @default.
- W3113179618 hasConcept C153180895 @default.
- W3113179618 hasConcept C154945302 @default.
- W3113179618 hasConcept C171036898 @default.
- W3113179618 hasConcept C188082385 @default.
- W3113179618 hasConcept C192562407 @default.
- W3113179618 hasConcept C202444582 @default.
- W3113179618 hasConcept C2776401178 @default.
- W3113179618 hasConcept C33923547 @default.
- W3113179618 hasConcept C41008148 @default.
- W3113179618 hasConcept C41895202 @default.
- W3113179618 hasConcept C50644808 @default.
- W3113179618 hasConcept C62520636 @default.
- W3113179618 hasConcept C74050887 @default.
- W3113179618 hasConceptScore W3113179618C121332964 @default.
- W3113179618 hasConceptScore W3113179618C138885662 @default.
- W3113179618 hasConceptScore W3113179618C153180895 @default.
- W3113179618 hasConceptScore W3113179618C154945302 @default.
- W3113179618 hasConceptScore W3113179618C171036898 @default.
- W3113179618 hasConceptScore W3113179618C188082385 @default.
- W3113179618 hasConceptScore W3113179618C192562407 @default.
- W3113179618 hasConceptScore W3113179618C202444582 @default.
- W3113179618 hasConceptScore W3113179618C2776401178 @default.
- W3113179618 hasConceptScore W3113179618C33923547 @default.
- W3113179618 hasConceptScore W3113179618C41008148 @default.
- W3113179618 hasConceptScore W3113179618C41895202 @default.
- W3113179618 hasConceptScore W3113179618C50644808 @default.
- W3113179618 hasConceptScore W3113179618C62520636 @default.
- W3113179618 hasConceptScore W3113179618C74050887 @default.
- W3113179618 hasFunder F4320329037 @default.
- W3113179618 hasFunder F4320335322 @default.
- W3113179618 hasLocation W31131796181 @default.
- W3113179618 hasOpenAccess W3113179618 @default.
- W3113179618 hasPrimaryLocation W31131796181 @default.
- W3113179618 hasRelatedWork W2016461833 @default.
- W3113179618 hasRelatedWork W2052253960 @default.
- W3113179618 hasRelatedWork W2147802381 @default.
- W3113179618 hasRelatedWork W2331674254 @default.
- W3113179618 hasRelatedWork W2382607599 @default.
- W3113179618 hasRelatedWork W2489255581 @default.
- W3113179618 hasRelatedWork W2760085659 @default.
- W3113179618 hasRelatedWork W2970216048 @default.
- W3113179618 hasRelatedWork W3197541072 @default.
- W3113179618 hasRelatedWork W376702462 @default.
- W3113179618 hasVolume "174" @default.
- W3113179618 isParatext "false" @default.
- W3113179618 isRetracted "false" @default.
- W3113179618 magId "3113179618" @default.
- W3113179618 workType "article" @default.