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集成高性能计算、机器学习、数据管理工作流程以及用于多尺度模拟和纳米材料技术的基础设施。

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

作者信息

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

机构信息

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

Department of Computer Science and Engineering, University of Bologna, Bologna, Via Zamboni 33, Italy.

出版信息

Beilstein J Nanotechnol. 2024 Nov 27;15:1498-1521. doi: 10.3762/bjnano.15.119. eCollection 2024.

DOI:10.3762/bjnano.15.119
PMID:39624205
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11610488/
Abstract

This perspective article explores the convergence of advanced digital technologies, including high-performance computing (HPC), artificial intelligence, machine learning, and sophisticated data management workflows. The primary objective is to enhance the accessibility of multiscale simulations and their integration with other computational techniques, thereby advancing the field of nanomaterials technologies. The proposed approach relies on key strategies and digital technologies employed to achieve efficient and innovative materials discovery, emphasizing a fully digital, data-centric methodology. The integration of methodologies rooted in knowledge and structured information management serves as a foundational element, establishing a framework for representing materials-related information and ensuring interoperability across a diverse range of tools. The paper explores the distinctive features of digital and data-centric approaches and technologies for materials development. It highlights the role of digital twins in research, particularly in the realm of nanomaterials development and examines the impact of knowledge engineering in establishing data and information standards to facilitate interoperability. Furthermore, the paper explores the role of deployment technologies in managing HPC infrastructures. It also addresses the pairing of these technologies with user-friendly development tools to support the adoption of digital methodologies in advanced research.

摘要

这篇观点文章探讨了先进数字技术的融合，包括高性能计算（HPC）、人工智能、机器学习和复杂的数据管理工作流程。主要目标是提高多尺度模拟的可及性及其与其他计算技术的集成，从而推动纳米材料技术领域的发展。所提出的方法依赖于用于实现高效和创新材料发现的关键策略和数字技术，强调完全数字化、以数据为中心的方法。植根于知识和结构化信息管理的方法的整合是一个基础要素，建立了一个表示材料相关信息的框架，并确保跨各种工具的互操作性。本文探讨了用于材料开发的数字和以数据为中心的方法及技术的独特特征。它强调了数字孪生在研究中的作用，特别是在纳米材料开发领域，并研究了知识工程在建立数据和信息标准以促进互操作性方面的影响。此外，本文探讨了部署技术在管理高性能计算基础设施中的作用。它还讨论了将这些技术与用户友好的开发工具相结合，以支持在先进研究中采用数字方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/087138ad58ac/Beilstein_J_Nanotechnol-15-1498-g016.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/261a9bf3815d/Beilstein_J_Nanotechnol-15-1498-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/f1bf2afff051/Beilstein_J_Nanotechnol-15-1498-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/8efd8091b0d2/Beilstein_J_Nanotechnol-15-1498-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/087138ad58ac/Beilstein_J_Nanotechnol-15-1498-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/acf99111d809/Beilstein_J_Nanotechnol-15-1498-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/19f1ce0a06e5/Beilstein_J_Nanotechnol-15-1498-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/b01f5a3f7e65/Beilstein_J_Nanotechnol-15-1498-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/bf2221099875/Beilstein_J_Nanotechnol-15-1498-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/4a13fb8f45f4/Beilstein_J_Nanotechnol-15-1498-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/8490cbd077b8/Beilstein_J_Nanotechnol-15-1498-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/1e09f29eccdb/Beilstein_J_Nanotechnol-15-1498-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/8442099a7a73/Beilstein_J_Nanotechnol-15-1498-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/261a9bf3815d/Beilstein_J_Nanotechnol-15-1498-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/716d29b26401/Beilstein_J_Nanotechnol-15-1498-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/c234d1b6ac8c/Beilstein_J_Nanotechnol-15-1498-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/0818a7c51f8d/Beilstein_J_Nanotechnol-15-1498-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/f1bf2afff051/Beilstein_J_Nanotechnol-15-1498-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/8efd8091b0d2/Beilstein_J_Nanotechnol-15-1498-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea71/11610488/087138ad58ac/Beilstein_J_Nanotechnol-15-1498-g016.jpg

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