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Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
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Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements.

So Takamoto1, Chikashi Shinagawa2, Daisuke Motoki2

  • 1Preferred Networks, Inc., 100-0004, 1-6-1 Otemachi, Chiyoda-ku, Tokyo, Japan. takamoto@preferred.jp.

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Summary

A new universal neural network potential (NNP), the PreFerred Potential (PFP), can simulate any combination of 45 elements, advancing computational material discovery. This broadly applicable tool enhances atomistic simulations for discovering novel materials.

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Area of Science:

  • Computational materials science
  • Machine learning in chemistry
  • Atomistic simulations

Background:

  • Computational material discovery aims to explore vast chemical spaces.
  • Neural network potentials (NNPs) are effective for atomistic simulations but often material-specific.
  • Existing NNPs lack universality, limiting their application in broad material discovery.

Purpose of the Study:

  • To develop a universal neural network potential (NNP) applicable to any combination of 45 elements.
  • To overcome the limitations of narrow-target NNPs in computational material discovery.
  • To provide a versatile tool for accelerating the discovery of new materials.

Main Methods:

  • Development of a universal NNP named PreFerred Potential (PFP).
  • Creation of diverse datasets including virtual structures to achieve universality.
  • Application and validation of PFP across various material systems and phenomena.

Main Results:

  • PFP demonstrates capability in simulating diverse systems, including lithium diffusion, molecular adsorption, alloy transitions, and catalyst discovery.
  • The universal nature of PFP allows for handling any combination of 45 elements.
  • The developed datasets are crucial for achieving the broad applicability of PFP.

Conclusions:

  • The PreFerred Potential (PFP) offers a universal solution for NNP-based atomistic simulations.
  • PFP significantly enhances the scope and efficiency of computational material discovery.
  • This technology represents a powerful and highly useful tool for researchers in materials science.