AutoMat benchmark shows current LLM coding agents achieve at most 54.1% success when reproducing computational materials science claims from papers.
Physical review , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
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A perturbative CCSD trial wavefunction renders AFQMC size-extensive with negligible bias, matching CISD-level accuracy on small systems while avoiding infrared divergence in the uniform electron gas thermodynamic limit unlike CCSD(T).
TriForces adds a model-agnostic three-stream architecture plus self-supervised objectives to atomistic GNNs, improving transfer performance on MatBench, QM9, and limited-data OMat24 without DFT labels.
citing papers explorer
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Size Extensive Auxiliary-Field Quantum Monte Carlo with Perturbative Coupled Cluster Trial Wavefunction
A perturbative CCSD trial wavefunction renders AFQMC size-extensive with negligible bias, matching CISD-level accuracy on small systems while avoiding infrared divergence in the uniform electron gas thermodynamic limit unlike CCSD(T).
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TriForces: Augmenting Atomistic GNNs for Transferable Representations
TriForces adds a model-agnostic three-stream architecture plus self-supervised objectives to atomistic GNNs, improving transfer performance on MatBench, QM9, and limited-data OMat24 without DFT labels.