Hugging Face Trending Papers

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.

arXiv Machine Learning

Jul 23

OLEDLM: A Unified Language Model for OLED Molecular Design

arXiv:2607. 20194v1 Announce Type: new Abstract: 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.

By Fukang Wen, Yuchong Tang, Jingyuan Li, Beichen Wang, Yixuan Jiang, Xiaoyi Jiang, Yaxuan Liu, Shunyu Wang, Zuoqiang Shi, Yi Zhu, Yanan Zhu, Pipi Hu

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 9

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

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.

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

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 22

Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation

arXiv:2607. 19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design.

By Mingxuan Ouyang, Hao Lan, Wanyu Lin

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 9

Closing the Prior-Posterior Loop: Self-Reflective Molecular Design with Analysis-Driven LLM Iteration

arXiv:2606. 09520v1 Announce Type: cross Abstract: Can a general-purpose large language model design molecules with the precision of a seasoned chemist?

By Junyi Gong, Zijie Qiu, Ben Zhong Tang

llms rag

arXiv AI

Jun 3

Rethinking Molecular Text Representations for LLMs: An Empirical Study

arXiv:2606. 03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use.

By Arun Raja, Garrett M. Morris, Kian Ming A. Chai

llms nlp benchmarks

arXiv AI

23h ago

Domain-Adapted Molecular Language Models for Efficient Search of Make-on-Demand Libraries

arXiv:2608. 17567v1 Announce Type: cross Abstract: Pretrained molecular language models are increasingly used as molecular encoders for learning structure-property relationships.

By Henrik Wille, Luis-Finley Sch\"utz, Felix Strieth-Kalthoff

llms rag robotics fine-tuning benchmarks

arXiv AI

Jul 7

Back to Basics: Improving Molecular Understanding in LLMs via SMILES-Graph Translation

arXiv:2607. 03007v1 Announce Type: cross Abstract: Recent advances in molecular large language models have led to strong performance on molecular understanding and generation tasks, yet these gains often come without reliable structural grounding.

By Wenda Wang, Jinjia Feng, Zhewei Wei

llms benchmarks

arXiv Machine Learning

Jun 8

Controllable Molecular Generative Foundation Models

arXiv:2605. 15354v2 Announce Type: replace Abstract: Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tasks with reliable controllability.

By Yihan Zhu, Yuhan Liu, Weijiang Li, Tengfei Luo, Meng Jiang

rag diffusion reinforcement-learning benchmarks

arXiv AI

Aug 7

Domain-Gated Latent Diffusion: Generative Inverse Design of HMX-Class Energetic Materials with First-Principles Validation

arXiv:2605. 26540v2 Announce Type: replace-cross Abstract: Energetic materials power mining, demolition, propulsion and airbags, yet today's compounds were designed decades ago.

By Yehudit Aperstein, Alexander Apartsin

diffusion benchmarks safety

arXiv Machine Learning

Jun 5

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry

arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.

By Joey Chan, Wonbin Kweon, Ashley Shin, Niharika Bhattacharjee, Pengcheng Jiang, Yue Guo, Jiawei Han

llms rag fine-tuning

arXiv Machine Learning

Jun 9

Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction

arXiv:2601. 09285v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge.

By Mianzhi Pan, JianFei Li, Peishuo Liu, Botian Wang, Yawen Ouyang, Yiming Rong, Hao Zhou, Jianbing Zhang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 11

Augmenting Molecular Language Models with Local $n$-gram Memory

arXiv:2606. 12113v1 Announce Type: cross Abstract: Transformer-based language models for SMILES strings suffer from a locality gap: standard character-level tokenization fragments chemically meaningful motifs, forcing models to repeatedly learn local syntax at the expense of long-range dependencies.

By Xinni Zhang, Zijing Liu, He Cao, Yu Li, Irwin King

llms rag nlp safety

Related stories