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1 article(s) from Vozza, Mario

Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies

Fabio Le Piane,
Mario Vozza,
Matteo Baldoni and
Francesco Mercuri

Beilstein J. Nanotechnol. 2024, 15, 1498–1521, doi:10.3762/bjnano.15.119

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Figure 1: Main digital technologies for materials innovation.

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Figure 2: Merging ML with HPC infrastructures can be done in three different ways: ML-in-HPC uses AI/ML surro...

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Figure 3: The funnel for the convergence of a manifold of digital technologies towards the materials domain. ...

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Figure 4: SWOT analysis of data-centric approaches in materials science.

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Figure 5: Main building blocks for a workflow comprising data collection, ML model training, and deployment o...

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Figure 6: SWOT analysis of digital twin applications for the materials science domain.

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Figure 7: The integration paradigm between knowledge and specific technologies for fully digital data-centric...

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Figure 8: The general architecture of a workflow-oriented data-driven framework for materials development. Th...

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Figure 9: SWOT analysis of semantic technologies in materials science.

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Figure 10: MAMBO main core classes and relationships: the ontology revolves around the concepts of Material, S...

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Figure 11: A visual description of the workflow discussed. The first block contains the input files, which are...

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Figure 12: An excerpt of a real-world input file containing structural information about a molecule encoded in...

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Figure 13: An excerpt of a real-world configuration file containing information about a simulation. This examp...

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Figure 14: SWOT analysis of the main rising digital technologies and their applications to the materials scien...

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Figure 15: SWOT analysis of infrastructural technologies applied to materials science.

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Perspective

Published 27 Nov 2024

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