Matches in SemOpenAlex for { <https://semopenalex.org/work/W2902452488> ?p ?o ?g. }
- W2902452488 abstract "Abstract Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples. We present the design and implementation of a deep neural network model referred to as ElemNet ; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed. The speed and best-in-class accuracy of ElemNet enable us to perform a fast and robust screening for new material candidates in a huge combinatorial space; where we predict hundreds of thousands of chemical systems that could contain yet-undiscovered compounds." @default.
- W2902452488 created "2018-12-11" @default.
- W2902452488 creator A5001525854 @default.
- W2902452488 creator A5004659592 @default.
- W2902452488 creator A5021403239 @default.
- W2902452488 creator A5027852342 @default.
- W2902452488 creator A5041088492 @default.
- W2902452488 creator A5074976770 @default.
- W2902452488 creator A5084730059 @default.
- W2902452488 date "2018-12-04" @default.
- W2902452488 modified "2023-10-14" @default.
- W2902452488 title "ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition" @default.
- W2902452488 cites W1548849615 @default.
- W2902452488 cites W1806891645 @default.
- W2902452488 cites W1865667476 @default.
- W2902452488 cites W1965154800 @default.
- W2902452488 cites W1965936134 @default.
- W2902452488 cites W1976492731 @default.
- W2902452488 cites W1984541135 @default.
- W2902452488 cites W1990277518 @default.
- W2902452488 cites W1992985800 @default.
- W2902452488 cites W2017277188 @default.
- W2902452488 cites W2026444402 @default.
- W2902452488 cites W2033169180 @default.
- W2902452488 cites W2034097448 @default.
- W2902452488 cites W2038881169 @default.
- W2902452488 cites W2041630184 @default.
- W2902452488 cites W2054511520 @default.
- W2902452488 cites W2055974394 @default.
- W2902452488 cites W2067590565 @default.
- W2902452488 cites W2068000900 @default.
- W2902452488 cites W2074604255 @default.
- W2902452488 cites W2078226033 @default.
- W2902452488 cites W2089843324 @default.
- W2902452488 cites W2111855693 @default.
- W2902452488 cites W2117363206 @default.
- W2902452488 cites W2142009706 @default.
- W2902452488 cites W2145992718 @default.
- W2902452488 cites W2151103935 @default.
- W2902452488 cites W2152152303 @default.
- W2902452488 cites W2152175008 @default.
- W2902452488 cites W2156854610 @default.
- W2902452488 cites W2157955200 @default.
- W2902452488 cites W2159357141 @default.
- W2902452488 cites W2160208155 @default.
- W2902452488 cites W2162897826 @default.
- W2902452488 cites W2164524421 @default.
- W2902452488 cites W2213612645 @default.
- W2902452488 cites W2261108203 @default.
- W2902452488 cites W2262229344 @default.
- W2902452488 cites W2278970271 @default.
- W2902452488 cites W2313966941 @default.
- W2902452488 cites W2329258563 @default.
- W2902452488 cites W2337110853 @default.
- W2902452488 cites W2338402873 @default.
- W2902452488 cites W2346180883 @default.
- W2902452488 cites W2347129741 @default.
- W2902452488 cites W2464725281 @default.
- W2902452488 cites W2490901606 @default.
- W2902452488 cites W2509907061 @default.
- W2902452488 cites W2520500207 @default.
- W2902452488 cites W2525025747 @default.
- W2902452488 cites W2527189750 @default.
- W2902452488 cites W2527749992 @default.
- W2902452488 cites W2565212977 @default.
- W2902452488 cites W2585666583 @default.
- W2902452488 cites W2593838212 @default.
- W2902452488 cites W2594183968 @default.
- W2902452488 cites W2616519837 @default.
- W2902452488 cites W2734520197 @default.
- W2902452488 cites W2760710953 @default.
- W2902452488 cites W2762665565 @default.
- W2902452488 cites W2777965033 @default.
- W2902452488 cites W2778051509 @default.
- W2902452488 cites W2884430236 @default.
- W2902452488 cites W2919115771 @default.
- W2902452488 cites W2963784900 @default.
- W2902452488 cites W2964350391 @default.
- W2902452488 cites W3098905070 @default.
- W2902452488 cites W3102449990 @default.
- W2902452488 cites W4211013046 @default.
- W2902452488 doi "https://doi.org/10.1038/s41598-018-35934-y" @default.
- W2902452488 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6279928" @default.
- W2902452488 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30514926" @default.
- W2902452488 hasPublicationYear "2018" @default.
- W2902452488 type Work @default.
- W2902452488 sameAs 2902452488 @default.
- W2902452488 citedByCount "223" @default.
- W2902452488 countsByYear W29024524882019 @default.
- W2902452488 countsByYear W29024524882020 @default.
- W2902452488 countsByYear W29024524882021 @default.
- W2902452488 countsByYear W29024524882022 @default.
- W2902452488 countsByYear W29024524882023 @default.
- W2902452488 crossrefType "journal-article" @default.
- W2902452488 hasAuthorship W2902452488A5001525854 @default.
- W2902452488 hasAuthorship W2902452488A5004659592 @default.
- W2902452488 hasAuthorship W2902452488A5021403239 @default.
- W2902452488 hasAuthorship W2902452488A5027852342 @default.
- W2902452488 hasAuthorship W2902452488A5041088492 @default.
- W2902452488 hasAuthorship W2902452488A5074976770 @default.