About - Registration - Schedule - Keynote Speakers - Keynote Abstracts - Accepted Contributions - Important Dates - Call for Papers
IMPORTANT
2024-09-18: Registration for the workshop is now open. Please register and join us!
About
Machine Learning models for molecules and materials are essential drivers in drug discovery, materials design, environmental science, and precision medicine. With these models, experts are enabled to meaningfully interact with molecular systems and to design them. The interaction with molecular systems allows for deeper insights into typically complex systems and facilitate (enable) the development of new drugs, personalized medical treatments, and innovative materials - e.g., advanced fuel cells in the field of energy conversion and storage or materials for environmental remediation in the field of environmental science. Recently, large Language Models (LLMs) have demonstrated remarkable performance across various domains in machine learning, revolutionizing natural language processing tasks and showing notable capabilities in understanding and generating human-like text. LLMs have also had a profound impact on molecular machine learning because they allow for human-AI interaction, interpretability, zero- and few-shot adaption, and in-context learning. However, the application of LLM architectures to molecular data presents significant challenges. Therefore, this workshop focuses on exploring potentials of ML methods with broader capabilities like LLMs incorporate them. We welcome all work which might provide potential steps towards these broader capabilities, e.g., by tackling the areas of knowledge and interaction, adaptability and robustness, abstraction and reasoning, or efficiency.
Join us at our workshop at which experts from diverse fields, ranging from ML and LLMs to molecular sciences, will collaborate to explore new potentials of ML methods for molecules and materials in the era of LLMs.
Registration
The workshop will be open to everyone without a registration fee. You can register here!
Schedule
Fri, Dec 6th, 09:00 am - 6:00 pm, CET; venue: Fritz Haber Institute of the Max Planck Society, Berlin; online at Zoom
| CET | Event | Speakers | Title |
|---|---|---|---|
| 1. Session | |||
| 09:00 | Opening remarks | ML4Mol Chair | |
| 09:00 | Invited Talk | Tim Duignan | Neural network potentials solve the connecting scales problem |
| 09:30 | Invited Talk | Gábor Csányi | |
| 10:00 | Contributed talk | ||
| 10:15 | Contributed talk | ||
| 10:30 | Contributed talk | ||
| 10:45 | Contributed talk | ||
| 11:00 | Poster Session 1 (PS 1) | Poster discussion | |
| 12:00 | Break | ||
| 2. Session | |||
| 13:00 | Invited talk | Cecilia Clementi | Modeling protein dynamics with machine learning and molecular simulation |
| 13:30 | Invited talk | Daniel Probst | Less is More |
| 14:00 | Invited talk | Igor Tetko | Expectations, achievements and lessons of AIDD and AIChemist Marie Skłodowska-Curie Innovative Training Network - European Industrial Doctorate projects |
| 14:30 | Contributed talk | ||
| 14:45 | Contributed talk | ||
| 15:00 | Break | ||
| 3. Session | |||
| 15:30 | Invited talk | Jutta Rogal | |
| 16:00 | Invited talk | Jian Tang | Geometric Deep Learning for Protein Design |
| 16:30 | Poster Session 2 (PS 2) | Poster discussion | |
| 17:00 | Retreat of ELLIS fellows | ||
| 18:00 | End & Closing remarks |
Keynote Speakers
Keynote Abstracts
[TBA]
Accepted contributions (oral)
[TBA]
Accepted contributions (poster)
[TBA]
Important dates
- November 1, 2024: Deadline for submission
- Mid November, 2024: Author notification
- December 6, 2024: Workshop
Call for papers
We are calling for papers exploring machine learning for molecules and materials in the era of LLMs. Topics include (but not limited to):
- Molecular Dynamics and Machine Learning
- Generative Models for Molecule and Material Design
- Molecular Property Prediction
- Inverse Design and Optimization in Materials Science
- Molecular Simulation Enhancement with ML
- Representation Learning for Molecules and Materials
- Machine learning methods for chemical reactions and synthesis
- Uncertainty Quantification and Robustness in Molecular ML Models
- Active Learning and Data-Efficient ML for Molecular Science
- Language models in chemistry
Please submit your contributions on OpenReview until November 1 2024 11:59 PM UTC-0. The submissions should be in PDF and follow the NeurIPS template with a maximum of 5 pages (not including references and appendices). We also welcome extended abstract submissions (2 pages). Please anonymize your paper since the review process is dual-anonymous. Filling the NeurIPS checklist with the paper is not compulsory.
Note that workshop papers often represent the current status of on-going projects. They should not be considered as final versions of record for a particular project. The workshop is non-archival.
Organizing Committee and Contact
Chairs: Johannes Margraf, Francesca Grisoni, Günter Klambauer
Organizing committee: Alaa Bessadok, Sovhi Luukkonen, Johannes Schimunek, Karsten Reuter, Giulia Glorani, Francesca Grisoni, Johannes Margraf, Günter Klambauer
Contact: ml4molecules@ml.jku.at