Workshop on AI for Semiconductor Discovery and Autonomous Materials Research 

🪧 Call for Posters

We welcome poster submissions from the community. If you would like to present a poster in person at the workshop, please submit your expression of interest via: 

👉 https://forms.gle/qRXxfw7ZeJuJGwzL8 

The workshop will begin at 9:00 (CEST) on May 21, 2026. The poster session will take place on Day 1 in two parts:

• 11:50 – 12:30

• 16:10 – 17:30

On May 22, 2026, the workshop will continue with invited talks and is expected to conclude at 12:00 (CEST).

Poster presenters are welcome to attend both days, but this is not required. Due to space constraints, we can accommodate up to 20 posters.

Submissions will be lightly reviewed. In particular, we will consider:

1. Clarity of the title and abstract

2. Relevance to the workshop topics. Please refer to the topics of interest below.

A registration fee of 50 EUR applies to all accepted poster presenters to attend the workshop.

 🌟Topics of Interest

The AISem'26 workshop invites poster abstracts across a wide range of contribution types—including original research, early-stage ideas, system demos, use cases, vision papers, and mature versions of preprints—on topics including, but not limited to, the following:

• AI for Semiconductor Discovery and Process Innovation

• Materials Informatics and Data-Driven Materials Science

• Machine Learning for Chemistry and Materials Design

• Large Language Models for Scientific Data Mining and Knowledge Extraction

• Autonomous Laboratories and AI Agents for Scientific Experimentation

• Process Optimization and Modeling in Semiconductor Manufacturing

• Integration of Experimental and Simulation Data

• Knowledge Extraction from Scientific Literature

• FAIR Data Principles and Machine-Actionable Scientific Knowledge

• Scientific Knowledge Graphs and Semantic Representations

• Reproducible and AI-Ready Scientific Datasets

• Automated and Human-in-the-Loop Workflows for Materials Discovery

• Digital Infrastructure for Scientific Experimentation and Data Management

 Acknowledgements