Introduces tree-line graph constructions and analyzes continuous quantum walks on their iterations and derived graphs via equitable partitions from corresponding tree graphs.
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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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.
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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.
- Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop
- Spatial statistics for screening molecular structures