Graph Neural Network Interatomic Potential (GNN-IP)

id: gnn_interatomic_potential· bridge construct

A machine-learned force field that maps crystal graph inputs to total energies, atomic forces, and stresses using message-passing neural networks. Trained on DFT trajectories (e.g., MPtrj ~1.6M structures), enabling geometry optimization at DFT accuracy but orders of magnitude faster. Examples: M3GNet, CHGNet, MACE-MP, SevenNet.

cross-pax synthesis This construct appears in 2 pax. Agents can run analyses that span all of them — findings from each pax are directly comparable because they share this construct definition.

// usage

Used in 2 pax

F field Bartel Comp Materials

↑ positive relationship 7 findings

F field Machine learning for computational materials discovery

↑ positive relationship 7 findings

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