The view from the IESC Inst.

TOTEMIC aims to explore the critical role of materials science in the ecological transition and raise awareness within the scientific community about the new paradigm of AI integration in materials development. This event will delve into the intersection of materials science, artificial intelligence, and multi-scale modeling.

Attendees will gain insights into the latest advancements in energy conversion, storage, and high-performance materials, as well as strategies for maximizing the utility of data generated in materials science, adhering to the FAIR principles. This school is primarily designed for PhD students and postdocs, but it also welcomes senior researchers seeking to incorporate AI and data-driven approaches into their work.

Register here

Why Attend?

TOTEMIC provides a unique opportunity to:

• Learn how AI techniques (machine learning, neural networks, etc.) can revolutionize energy materials research.
• Gain hands-on experience with state-of-the-art tools for modeling and characterizing materials.
• Engage with leading experts and peers in materials science and AI.
• Contribute to the global push for sustainable energy solutions.

Scientific Focus Areas:

1. AI in Materials Science:

   • Neural networks, machine learning, deep learning, and optimization processes tailored to energy materials research.
   • Practical training on identifying and applying the right digital tools for AI integration.

2. Advanced Modeling Approaches:

   • The evolution of numerical modeling techniques (e.g., DFT, molecular dynamics, finite elements) enhanced by AI for multiscale and multiphysics analysis.
   • Applications in atomistic approaches for energy materials.

3. Data Management and Open Science:

   • Harnessing materials science databases for efficient AI-driven research (FAIR principles).
   • Best practices for documenting and sharing scientific data to accelerate discoveries.

4. Community Building:

   • Strengthening collaborations between networks like COST Action EU-MACE and GDR CNRS NAME.
   • Encouraging partnerships for future EU Horizon Europe proposals.

Teaching Format:

The school will run over one week (Monday to Friday), featuring:

• Core Lectures: Foundational and advanced topics in AI-driven materials science.
• Applied Sessions: Hands-on sessions with numerical and AI tools.
• Poster Session: Showcase research from young participants, with interactive discussions.
• Round-Table Discussion: A collaborative forum to explore future directions in materials science and AI.
  Participants will receive comprehensive teaching materials and resources online.

Who Should Attend?

• Priority:

  • Doctoral and postdoctoral researchers developing or using simulation tools for materials science who want to integrate AI into their workflows.

• Other Participants:

  • Early-career and experienced researchers, industry professionals, and EPIC staff interested in incorporating AI into materials modeling or accelerating their research with advanced digital tools.

Prerequisites:

Participants should meet the following requirements:

• Master’s (M2) level in physics, solid-state chemistry, or materials science.
• Basic knowledge of numerical methods (refresher MOOCs will be recommended before the event).

For more info on the application and registration procedure please consult the info pack. The detailed programme updated as per 09/12/2024 is available here.

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