inverse-design

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papersTODAY 04:00 UTC

LLM4MOF: Multi-Agent Framework for Inverse Design of Metal-Organic Frameworks

Researchers propose LLM4MOF, a closed-loop multi-agent system that turns natural-language design requests into candidate metal-organic frameworks and evaluates them iteratively. The approach aims to make inverse design of these porous crystalline materials more interpretable, addressing combinatorial search spaces and costly property labels. The work appears as a replacement submission on arXiv in the cs.AI and cs.LG categories.

agents/llm/materials-science/interpretability

papersTODAY 04:00 UTC

Reinforcement learning approach generates crystals with target symmetry and properties

A new arXiv preprint presents a reinforcement learning method for inverse design of crystalline materials that treats crystal symmetry as a constraint alongside desired physical properties. The authors argue that a good numerical property value is only meaningful when the structure's underlying symmetry is appropriate, so their approach generates candidates that satisfy both. The work targets functional materials discovery where symmetry-aware generation matters.

materials-science/reinforcement-learning/generative-models/arxiv