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arXiv Machine Learning By Tianren Zhang

Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation

Read the original on arXiv Machine Learning →

The paper introduces PolyLatentFlow, a continuous‑time flow‑matching framework for polymer generation, and LlamaUni, a multimodal representation that fuses polymer sequences with 3D structural data. In unconditional generation, the combination yields the highest number of valid, novel candidates while preserving diversity, and in conditional settings it systematically shifts property distributions across a 200 °C target range. Across multi‑property tasks, the representation choice affects validity, training‑set replay, and structural proximity, with PolyLatentFlow + LlamaUni achieving the best balance of high validity, low replay, and high target hit yield.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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