arXiv AI June 2, 2026 By Edward W. Staley, Tom Arbaugh, Michael Pekala, Alexander New, Christopher D. Stiles, Nam Q. Le, Gregory Bassen, Wyatt Bunstine, Tyrel McQueen

Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials

Read the original on arXiv AI →

arXiv:2606. 00315v1 Announce Type: new Abstract: Modern generative machine learning (ML) models can propose novel inorganic crystalline materials with targeted properties; however, synthesis planning of these materials remains difficult due to the complexity of the associated physical processes and limited availability of computational tools.

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

llms

arXiv AI

Jun 3

Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation

arXiv:2604. 13354v2 Announce Type: replace-cross Abstract: The discovery of inorganic crystal structures with targeted properties is a significant challenge in materials science.

By Auguste de Lambilly, Vladimir Baturin, David Portehault, Guillaume Lambard, Nataliya Sokolovska, Florence d'Alch\'e-Buc, Jean-Claude Crivello

diffusion fine-tuning benchmarks

arXiv Machine Learning

Aug 12

Inverse Design of Inorganic Compounds with Generative AI

arXiv:2604. 11827v2 Announce Type: replace-cross Abstract: Machine learning is revolutionizing chemistry.

By Hannes Kneiding, Luc\'ia Mor\'an-Gonz\'alez, Nishamol Kuriakose, Ainara Nova, David Balcells

benchmarks

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

Hugging Face Trending Papers

Jul 20

Chemical filters for ultra-high-throughput materials screening and generation

Generative artificial intelligence is rapidly transforming materials design by enabling de novo exploration of immense chemical spaces. Yet a large proportion of AI-generated compositions remain implausible, violating established chemical principles, which limits the reliability and interpretability of generative materials design.

diffusion efficiency benchmarks safety

arXiv AI

Jul 21

Chemical filters for ultra-high-throughput materials screening and generation

arXiv:2607. 17910v1 Announce Type: cross Abstract: Generative artificial intelligence is rapidly transforming materials design by enabling de novo exploration of immense chemical spaces.

By Kinga O. Mastej, Panyalak Detrattanawichai, Hyunsoo Park, Anthony Onwuli, Masahiro Negishi, Aron Walsh

diffusion efficiency benchmarks safety

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

Related stories

Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation

Inverse Design of Inorganic Compounds with Generative AI

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

Chemical filters for ultra-high-throughput materials screening and generation

Chemical filters for ultra-high-throughput materials screening and generation

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