An autonomous LLM coding agent built the top-performing crystal graph network on the MatBench band-gap benchmark by implementing known methods, outperforming 17 expert models without pretraining.
Atomistic line graph neural network for improved materials property predictions
4 Pith papers cite this work, alongside 692 external citations. Polarity classification is still indexing.
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Introduces tree-line graph constructions and analyzes continuous quantum walks on their iterations and derived graphs via equitable partitions from corresponding tree graphs.
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
citing papers explorer
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Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop
An autonomous LLM coding agent built the top-performing crystal graph network on the MatBench band-gap benchmark by implementing known methods, outperforming 17 expert models without pretraining.
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Tree-Line graphs and their quantum walks
Introduces tree-line graph constructions and analyzes continuous quantum walks on their iterations and derived graphs via equitable partitions from corresponding tree graphs.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
- Spatial statistics for screening molecular structures