arXiv Machine Learning June 15, 2026 By Yusei Ito, Yuta Suzuki, Tomoya Murata, Masaki Adachi

LapidaryEngine: Fully Conversational Crystal Generation

Read the original on arXiv Machine Learning →

arXiv:2606. 14215v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) has inspired the vision of generating bespoke crystal materials directly from natural-language instructions, enabling users to design materials through intuitive, conversational interaction.

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

arXiv AI

Jun 9

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.

By Yanjie Li, Jian Xu, Xu-Yao Zhang, Shiming Xiang, Nian Ran, Weijun Li, Cheng-Lin Liu

llms multimodal safety

arXiv AI

Jun 2

Language-Native Materials Processing Design by Lightly Structured Text Database and Reasoning Large Language Model

arXiv:2509. 06093v4 Announce Type: replace-cross Abstract: Materials synthesis procedures are predominantly documented as narrative text in papers, protocols, and laboratory records, placing them beyond the reach of conventional data-driven optimization frameworks.

By Yuze Liu, Zhaoyuan Zhang, Xiangsheng Zeng, Yihe Zhang, Leping Yu, Liu Yang, Lejia Wang, Xi Yu

llms rag

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 9

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

arXiv:2606. 07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems.

By Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu, Xiaoyu Wu, Rongyan Wang, Linjing Li, Dajun Zeng, Yudong Yao, Tingwei Chen, Youwei Wang, Xiaolin Zhao, Jiahui Shi, Jianjun Liu

llms diffusion reinforcement-learning

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 Machine Learning

Aug 5

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language

arXiv:2608. 03855v1 Announce Type: new Abstract: Transformer models have revolutionized natural language processing (NLP), and text-based molecular representations like SMILES have successfully extended these architectures to chemistry.

By David Ming Segura, Jeremy Goumaz, Joshua W. Sin, Bojana Rankovi\'c, Philippe Schwaller

llms rag nlp

Related stories

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

Language-Native Materials Processing Design by Lightly Structured Text Database and Reasoning Large Language Model

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

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language