Recommanded Product: 6H-Benzo[c]chromen-6-one. About 6H-Benzo[c]chromen-6-one, If you have any questions, you can contact Vriza, A; Canaj, AB; Vismara, R; Cook, LJK; Manning, TD; Gaultois, MW; Wood, PA; Kurlin, V; Berry, N; Dyer, MS; Rosseinsky, MJ or concate me.
Recommanded Product: 6H-Benzo[c]chromen-6-one. Vriza, A; Canaj, AB; Vismara, R; Cook, LJK; Manning, TD; Gaultois, MW; Wood, PA; Kurlin, V; Berry, N; Dyer, MS; Rosseinsky, MJ in [Vriza, Aikaterini; Canaj, Angelos B.; Vismara, Rebecca; Cook, Laurence J. Kershaw; Manning, Troy D.; Gaultois, Michael W.; Berry, Neil; Dyer, Matthew S.; Rosseinsky, Matthew J.] Univ Liverpool, Dept Chem, 51 Oxford St, Liverpool L7 3NY, Merseyside, England; [Vriza, Aikaterini; Canaj, Angelos B.; Vismara, Rebecca; Cook, Laurence J. Kershaw; Manning, Troy D.; Gaultois, Michael W.; Berry, Neil; Dyer, Matthew S.; Rosseinsky, Matthew J.] Univ Liverpool, Mat Innovat Factory, 51 Oxford St, Liverpool L7 3NY, Merseyside, England; [Vriza, Aikaterini; Gaultois, Michael W.; Dyer, Matthew S.; Rosseinsky, Matthew J.] Univ Liverpool, Leverhulme Res Ctr Funct Mat Design, Oxford St, Oxford, England; [Wood, Peter A.] Cambridge Crystallog Data Ctr, 12 Union Rd, Cambridge CB2 1EZ, England; [Kurlin, Vitaliy] Univ Liverpool, Dept Comp Sci, Mat Innovat Factory, Liverpool L69 3BX, Merseyside, England published One class classification as a practical approach for accelerating pi-pi co-crystal discovery in 2021.0, Cited 81.0. The Name is 6H-Benzo[c]chromen-6-one. Through research, I have a further understanding and discovery of 2005-10-9.
The implementation of machine learning models has brought major changes in the decision-making process for materials design. One matter of concern for the data-driven approaches is the lack of negative data from unsuccessful synthetic attempts, which might generate inherently imbalanced datasets. We propose the application of the one-class classification methodology as an effective tool for tackling these limitations on the materials design problems. This is a concept of learning based only on a well-defined class without counter examples. An extensive study on the different one-class classification algorithms is performed until the most appropriate workflow is identified for guiding the discovery of emerging materials belonging to a relatively small class, that being the weakly bound polyaromatic hydrocarbon co-crystals. The two-step approach presented in this study first trains the model using all the known molecular combinations that form this class of co-crystals extracted from the Cambridge Structural Database (1722 molecular combinations), followed by scoring possible yet unknown pairs from the ZINC15 database (21 736 possible molecular combinations). Focusing on the highest-ranking pairs predicted to have higher probability of forming co-crystals, materials discovery can be accelerated by reducing the vast molecular space and directing the synthetic efforts of chemists. Further on, using interpretability techniques a more detailed understanding of the molecular properties causing co-crystallization is sought after. The applicability of the current methodology is demonstrated with the discovery of two novel co-crystals, namely pyrene-6H-benzo[c]chromen-6-one (1) and pyrene-9,10-dicyanoanthracene (2).
Recommanded Product: 6H-Benzo[c]chromen-6-one. About 6H-Benzo[c]chromen-6-one, If you have any questions, you can contact Vriza, A; Canaj, AB; Vismara, R; Cook, LJK; Manning, TD; Gaultois, MW; Wood, PA; Kurlin, V; Berry, N; Dyer, MS; Rosseinsky, MJ or concate me.
Reference:
Article; Zhang, Jian; Shi, Dongdong; Zhang, Haifeng; Xu, Zheng; Bao, Hanyang; Jin, Hongwei; Liu, Yunkui; Tetrahedron; vol. 73; 2; (2017); p. 154 – 163;,
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