The AI Scientist framework enables LLMs to independently conduct the full scientific process from idea generation to paper writing and review, demonstrated across three ML subfields with papers costing under $15 each.
Scaling deep learning for materials discovery
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UNVERDICTED 3representative citing papers
OXtal recovers experimental organic crystal structures with conformer RMSD below 0.5 Å and over 80% packing similarity using a lattice-free diffusion model trained on 600K structures.
SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
citing papers explorer
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The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
The AI Scientist framework enables LLMs to independently conduct the full scientific process from idea generation to paper writing and review, demonstrated across three ML subfields with papers costing under $15 each.
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OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
OXtal recovers experimental organic crystal structures with conformer RMSD below 0.5 Å and over 80% packing similarity using a lattice-free diffusion model trained on 600K structures.
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Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions
SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.