arXiv Machine Learning June 9, 2026 By Haozhe Huang, Manuel Gonzalez Lastre, Hyun Suk Park, Jorge A. Campos-Gonzalez-Angulo, Xinjian Liu, Al\'an Aspuru-Guzik

De novo molecular generation with optical property preconditioning at the token level

Read the original on arXiv Machine Learning →

arXiv:2606. 08221v1 Announce Type: new Abstract: Designing OLED molecules with targeted optical properties remains challenging due to the scarcity of high-quality data and the limited reliability of conditional control in generative models across chemical motifs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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OLEDLM: A Unified Language Model for OLED Molecular Design

The development of organic light-emitting diode (OLED) materials faces the compounded challenges of an astronomically large chemical space, stringent quantum-chemical constraints, and a scarcity of labeled data. Although the question of OLED generation is important, few models have been trained effectively for this specific domain.

llms reinforcement-learning fine-tuning

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OLEDLM: A Unified Language Model for OLED Molecular Design

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