arXiv AI June 9, 2026 By Yanjie Li, Jian Xu, Xu-Yao Zhang, Shiming Xiang, Nian Ran, Weijun Li, Cheng-Lin Liu

CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials

arXiv:2605. 17254v3 Announce Type: replace Abstract: Property prediction and inverse structural design of catalytic materials are typically modeled as two independent tasks: the former predicts target properties from given structures, whereas the latter generates candidate structures according to desired properties.

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

arXiv Machine Learning

Jun 2

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design

arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.

By Anand Babu, Rog\'erio Almeida Gouv\^ea, Gian-Marco Rignanese

diffusion reinforcement-learning multimodal

arXiv Machine Learning

Jun 17

Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining

arXiv:2606. 17445v1 Announce Type: new Abstract: Inverse design of heterogeneous catalysts remains challenging because catalyst surfaces exhibit substantial structural complexity with coupled surface-adsorbate interactions across a vast chemical space that is difficult to explore efficiently through conventional screening alone.

By Dong Hyeon Mok, Jonggeol Na, Seoin Back

llms rag fine-tuning efficiency

arXiv Machine Learning

Jul 27

Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives

arXiv:2607. 21660v1 Announce Type: cross Abstract: Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel materials discovery.

By Xianyuan Liu, Charles Anjah, Benjamin E. Jolly, Jonathon F. S. Markanday, Joshua Berry, Haolin Wang, Nicola A. Morley, Robert D. J. Oliver, Alexandra J. Ramadan, Delvin Ce Zhang, Katerina A. Christofidou, Haiping Lu

multimodal benchmarks safety

arXiv AI

Jun 11

MLaGA: Multimodal Large Language and Graph Assistant

arXiv:2506. 02568v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated substantial efficacy in advancing graph-structured data analysis.

By Dongzhe Fan, Yi Fang, Jiajin Liu, Djellel Difallah, Qiaoyu Tan

llms fine-tuning multimodal

arXiv Machine Learning

Jun 26

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning

arXiv:2602. 22284v3 Announce Type: replace Abstract: Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) domain.

By Mingi Kim, Yongjun Kim, Jungwoo Kang, Hyungki Kim

llms agents multimodal

arXiv AI

Jun 17

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?

arXiv:2602. 03300v2 Announce Type: replace-cross Abstract: In this work, we aim to develop effective data synthesis techniques that autonomously synthesize multimodal training data for enhancing MLLMs in solving complex real-world tasks.

By Jingyi Zhang, Tianyi Lin, Huanjin Yao, Xiang Lan, Shunyu Liu, Jiaxing Huang

llms multimodal benchmarks safety

Related stories

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design

Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining

Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives

MLaGA: Multimodal Large Language and Graph Assistant

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?