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[2602.22251] Zatom-1: A Multimodal Flow Foundation Model for 3D Molecules and Materials
Summary
Zatom-1 is a groundbreaking multimodal flow foundation model designed for 3D molecules and materials, enhancing both generative and predictive capabilities in chemical modeling.
Why It Matters
This research addresses the limitations of existing AI models that focus on either molecules or materials, offering a unified approach that improves predictive accuracy and reduces inference time. Its implications extend to various fields, including materials science and drug discovery, making it a significant advancement in AI-driven chemical modeling.
Key Takeaways
- Zatom-1 integrates generative and predictive learning for 3D chemical modeling.
- It utilizes a multimodal flow matching objective for improved performance.
- The model shows significant predictive transfer between chemical domains.
- Empirical results indicate faster generative inference times by over an order of magnitude.
- Zatom-1 outperforms specialized baselines in both generative and predictive tasks.
Computer Science > Machine Learning arXiv:2602.22251 (cs) [Submitted on 24 Feb 2026] Title:Zatom-1: A Multimodal Flow Foundation Model for 3D Molecules and Materials Authors:Alex Morehead, Miruna Cretu, Antonia Panescu, Rishabh Anand, Maurice Weiler, Tynan Perez, Samuel Blau, Steven Farrell, Wahid Bhimji, Anubhav Jain, Hrushikesh Sahasrabuddhe, Pietro Lio, Tommi Jaakkola, Rafael Gomez-Bombarelli, Rex Ying, N. Benjamin Erichson, Michael W. Mahoney View a PDF of the paper titled Zatom-1: A Multimodal Flow Foundation Model for 3D Molecules and Materials, by Alex Morehead and 16 other authors View PDF HTML (experimental) Abstract:General-purpose 3D chemical modeling encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimized for a single domain (molecules or materials) and a single task (generation or prediction), which limits representation sharing and transfer. We introduce Zatom-1, the first foundation model that unifies generative and predictive learning of 3D molecules and materials. Zatom-1 is a Transformer trained with a multimodal flow matching objective that jointly models discrete atom types and continuous 3D geometries. This approach supports scalable pretraining with predictable gains as model capacity increases, while enabling fast and stable sampling. We use joint generative pretraining as a universal initialization for downstream multi-task prediction of properties, energies, and ...
