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The Mendeleev-Meyer force project.

Sergio Santos1, Chia-Yun Lai2, Carlo A Amadei3

  • 1Laboratory for Energy and NanoScience (LENS), Institute Center for Future Energy (iFES), Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates. mchiesa@masdar.ac.ae and Future Synthesis, Adderbury Banbury, Oxfordshire, OX17 3EP, UK.

Nanoscale
|October 8, 2016
PubMed
Summary
This summary is machine-generated.

The Mendeleev-Meyer Force Project tabulates materials by nanoscale force footprints, not atomic properties. This approach uses deep learning to create searchable databases for material identification and property prediction.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Data Science

Background:

  • The periodic table organizes elements by atomic number and electron configuration.
  • A new approach is needed to categorize materials based on their physical interactions.

Purpose of the Study:

  • To develop a novel classification system for materials and substances.
  • To create searchable databases for nanoscale force data.
  • To enable material identification and property prediction using advanced algorithms.

Main Methods:

  • Acquiring nanoscale force data from diverse materials.
  • Parameterizing raw data into standardized features for a comprehensive library.
  • Employing deep learning algorithms, specifically multilayer neural networks, for data analysis and model development.

Main Results:

  • A framework for tabulating materials based on nanoscale force footprints has been established.
  • A prototype system utilizing deep learning for material identification is presented.
  • The project aims to create databases analogous to PRIDE and MEDLARS, with search functionalities similar to PubMed.

Conclusions:

  • The Mendeleev-Meyer Force Project offers a new paradigm for materials classification.
  • This approach has the potential to accelerate materials discovery and research.
  • The developed methods and databases will enhance the accessibility and utility of materials data.