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cond-mat.mtrl-sci2025
Autonomous Inorganic Materials Discovery via Multi-Agent Physics-Aware Scientific Reasoning
Alireza Ghafarollahi, Markus J. Buehler
Conventional machine learning approaches accelerate inorganic materials design via accurate property prediction and targeted material generation, yet they operate as single-shot mo…
cond-mat.mtrl-sci2025
Revealing diatom-inspired materials multifunctionality
Ludovico Musenich, Daniele Origo, Filippo Gallina +2
Diatoms have been described as nanometer-born lithographers because of their ability to create sophisticated three-dimensional amorphous silica exoskeletons. The hierarchical archi…
cond-mat.mtrl-sci2024
Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems
Alireza Ghafarollahi, Markus J. Buehler
A multi-agent AI model is used to automate the discovery of new metallic alloys, integrating multimodal data and external knowledge including insights from physics via atomistic si…