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Integrating high-performance computing, machine learning, data management workflows, and infrastructures for

Fabio Le Piane1,2, Mario Vozza1,3, Matteo Baldoni1

  • 1DAIMON Lab, CNR-ISMN, Bologna, via Gobetti 101, Italy.

Beilstein Journal of Nanotechnology
|December 3, 2024
PubMed
Summary
This summary is machine-generated.

This perspective explores how digital technologies like artificial intelligence and high-performance computing (HPC) can accelerate nanomaterials discovery. A data-centric approach enhances accessibility and integration for innovative materials development.

Keywords:
HPCartificial intelligencehigh-performance computingmachine learningmaterials modellingmultiscale modellingnanomaterialssemantic data management

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Area of Science:

  • Materials Science and Engineering
  • Computational Science
  • Digital Technologies

Background:

  • Advanced digital technologies are transforming scientific research and development.
  • Integrating high-performance computing (HPC), artificial intelligence (AI), and machine learning (ML) offers new avenues for materials discovery.
  • Current challenges include enhancing the accessibility and interoperability of complex computational tools and data.

Purpose of the Study:

  • To explore the convergence of advanced digital technologies for materials development.
  • To enhance the accessibility and integration of multiscale simulations and computational techniques in nanomaterials research.
  • To emphasize a fully digital, data-centric methodology for efficient and innovative materials discovery.

Main Methods:

  • Leveraging high-performance computing (HPC), artificial intelligence (AI), and machine learning (ML).
  • Implementing sophisticated data management workflows and structured information management.
  • Utilizing digital twins and knowledge engineering for data and information standardization.

Main Results:

  • A framework for representing materials-related information and ensuring interoperability across diverse tools.
  • Demonstration of digital twins' role in nanomaterials development.
  • Identification of deployment technologies for managing HPC infrastructures.

Conclusions:

  • A digital, data-centric methodology is crucial for advancing nanomaterials technologies.
  • Knowledge engineering and interoperability standards are foundational for effective materials development.
  • User-friendly tools paired with advanced technologies facilitate the adoption of digital methodologies in research.