Web of Science: 3 cites, Scopus: 3 cites, Google Scholar: cites
The Mendeleev-Meyer force project
Santos, Sergio (Future Synthesis. Adderbury Banbury)
Lai, Chia-Yun (Masdar Institute of Science and Technology. Institute Center for Future Energy. Laboratory for Energy and NanoScience)
Amadei, Carlo A. (Harvard University. John A. Paulson School of Engineering and Applied Sciences)
Gadelrab, Karim R. (Massachusetts Institute of Technology. Department of Materials Science and Engineering)
Tang, Tzu-Chieh (Massachusetts Institute of Technology. Department of Materials Science and Engineering)
Verdaguer Prats, Albert (Institut Català de Nanociència i Nanotecnologia)
Barcons, Victor (Universitat Politècnica de Catalunya. Departament de Disseny i Programació de Sistemes Electrònics)
Font, Josep (Universitat Politècnica de Catalunya. Departament de Disseny i Programació de Sistemes Electrònics)
Colchero, Jaime (Universidad de Murcia. Instituto de Óptica y Nanociencia)
Chiesa, Matteo (Masdar Institute of Science and Technology. Institute Center for Future Energy. Laboratory for Energy and NanoScience)

Data: 2016
Resum: Here we present the Mendeleev-Meyer Force Project which aims at tabulating all materials and substances in a fashion similar to the periodic table. The goal is to group and tabulate substances using nanoscale force footprints rather than atomic number or electronic configuration as in the periodic table. The process is divided into: (1) acquiring nanoscale force data from materials, (2) parameterizing the raw data into standardized input features to generate a library, (3) feeding the standardized library into an algorithm to generate, enhance or exploit a model to identify a material or property. We propose producing databases mimicking the Materials Genome Initiative, the Medical Literature Analysis and Retrieval System Online (MEDLARS) or the PRoteomics IDEntifications database (PRIDE) and making these searchable online via search engines mimicking Pubmed or the PRIDE web interface. A prototype exploiting deep learning algorithms, i. e. multilayer neural networks, is presented.
Drets: Tots els drets reservats.
Llengua: Anglès
Document: Article ; recerca ; Versió acceptada per publicar
Matèria: Atomic numbers ; Electronic configuration ; Force footprint ; Input features ; Medical literatures ; Parameterizing ; Periodic table ; Retrieval systems
Publicat a: Nanoscale, Vol. 8, Issue 40 (October 2016) , p. 17400-17406, ISSN 2040-3372

DOI: 10.1039/c6nr06094c


Postprint
20 p, 1.8 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > Institut Català de Nanociència i Nanotecnologia (ICN2)
Articles > Articles de recerca
Articles > Articles publicats

 Registre creat el 2019-09-23, darrera modificació el 2021-08-01



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