6 papers
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…
Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles
Alireza Ghafarollahi, Markus J. Buehler
Advances in artificial intelligence (AI) promise autonomous discovery, yet most systems still resurface knowledge latent in their training data. We present Sparks, a multi-modal mu…
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…
SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
Alireza Ghafarollahi, Markus J. Buehler
A key challenge in artificial intelligence is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex pat…
AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence
Alireza Ghafarollahi, Markus J. Buehler
The design of alloys is a multi-scale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting exp…
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
A. Ghafarollahi, M. J. Buehler
Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protei…