Call for papers

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Submitting authors (students and postdocs) of accepted short papers will be granted free admission. Members of industry will still need to purchase admission via regular registration.  

 

Four outstanding paper submissions will be invited to present their work in 20 minute oral presentations at the conference, with one winner of the Octavian-Eugen Ganea Prize for Best Paper awarded a cash prize of $1,526.

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We welcome short papers related to machine learning for molecules, from small drug-like molecules to proteins and organic crystals. Short papers can be submitted via the form below where you have to provide a 2 to 4 page .pdf file explaining your work. References, acknowledgments, and appendices do not count towards the page limit.

Here is an example of a possible format of a short paper and optional latex style files that you can use.

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Authors of short papers retain full copyright of their work, and acceptance does not preclude publication of the same material at another venue. Also, submissions that are under review or have been recently published are allowed for submission. Authors must ensure that they are not violating any other venue's dual submission policies.

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Submission guidelines

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Topics of interest include but are not limited to:

• Geometric machine learning for molecules

• Molecular graph machine learning

• Molecule/Protein 3D structure prediction and processing

• Modeling molecular interactions

• Molecular dynamics

• Machine learning for quantum and materials chemistry

 

Short papers must be submitted by October 3, 2025, 11:59 PM AOE. After our review phase, decisions will be communicated by email by October 10, 2025. Authors are expected to present their short paper via a poster in-person during the poster presentation sessions on October 22, 2025. Below is an example poster together with templates for creating a poster.

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NOTE: MoML does not have official proceedings so submission will not interfere with any dual submission policies for conferences with official proceedings (like ICLR).