AI4Chemistry&Materials
We invite submissions to the 2nd International Conference on AI for Science (AI4Sci 2026), welcoming abstracts on applications of artificial intelligence in and for science. AI4Sci 2026 is part of the AI4Science Week and co-located with the 29th International Conference on Discovery Science (DS 2026). AI4Sci features separate AI4Physics, AI4Chemistry&Materials, AI4LifeSciences, and AI4Humanities&SocialSciences tracks dealing with applications of AI and Machine Learning in a variety of different fields. Contrary to DS 2026, the AI4Sci tracks are non-archival and also suited to ongoing or already published work.
The AI4Chemistry&Materials Track of AI4Sci 2026 aims to bring together experts at the intersection of artificial intelligence, (bio)chemistry, and materials science to explore how data-driven approaches are transforming discovery, design, and deployment.
We welcome submissions presenting original research, case studies, and forward-looking perspectives. Contributions may address theoretical advances, methodological developments, or real-world applications. We also explicitly welcome contributions reporting negative results and failure cases, as these are essential for the progress of the field.
The track invites contributions on topics such as, but not restricted to:
- Real-world applications (including wet lab validation) of molecules and materials designed using generative AI
- Molecular property prediction across diverse chemical spaces
- The impact of uncertainty, robustness, and interpretability in model development and deployment
- Innovations in improving "AI readiness" across multiple data modalities (e.g., images, text, molecular structures, spectroscopy data) and their multimodal integration
- Learning from small and low-data regimes in chemistry and materials science
- AI-driven synthesis planning and reaction prediction
- Integration of simulation, automation, and machine learning in laboratory workflows
- Foundation models, large language models, and large-scale pretraining for chemistry and materials science
- Data curation, benchmarking, and reproducibility in chemical AI
- Autonomous experimentation and self-driving laboratories
- AI for sustainable chemistry and materials discovery
- Ethical, safety, and regulatory considerations in human-AI interaction and AI-driven chemical discovery
The talks of the track will be 15 minutes long, followed by 5 minutes of Q&A.
Submission Procedure
Submitted abstracts must be written in English and should not exceed 500 words, excluding references. The reviewing process will be single-blind. Thus, the submitted abstracts should not be anonymized.
The abstract should be entered in the field "Abstract" of the CMT system directly. Please use the PDF upload for potential supplementary material, for instance, of an already published paper based on which you want to give a talk at the track.
If accepted, we will ask you to submit a two-page PDF of the abstract in Springer LNCS style. A book of abstract will be provided at the conference. Publication of the book of abstracts at, e.g., https://pos.sissa.it/ or https://ceur-ws.org/ is under discussion with the track chairs.
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
Please submit your contributions at:
https://cmt3.research.microsoft.com/AIforSci2026/.
When submitting, please choose the track "AI4Chemistry&Materials".
The timeline for AI4Sci 2026 is as follows:
- CMT submission system opens: May 8th, 2026
- Abstract submission deadline: June 12th, 2026
- Notification of acceptance or rejection: July 21st, 2026
All deadlines are 23:59 AOE.
Track chairs
Paul Czodrowski
Johannes Gutenberg University Mainz
czodpaul@uni-mainz.de
Francesca Grisoni
TU Eindhoven
f.grisoni@tue.nl