DPA4 is a new SE(3)-equivariant interatomic potential with EMFA SO(2) convolution that sets new accuracy-cost records on Matbench Discovery and SPICE benchmarks using fewer parameters than prior models.
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4 Pith papers cite this work, alongside 66 external citations. Polarity classification is still indexing.
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Interfacial energetics computed with machine-learned potentials explain why Mn addition switches grain-boundary precipitates in Cu-Ni-Si alloys from irregular Ni2Si to film-shaped Mn6Ni16Si7.
Density diversity in training data is the key factor for making machine learning interatomic potentials transferable across thermodynamic states, outperforming temperature diversity.
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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DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
DPA4 is a new SE(3)-equivariant interatomic potential with EMFA SO(2) convolution that sets new accuracy-cost records on Matbench Discovery and SPICE benchmarks using fewer parameters than prior models.
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Precipitate phase selection and grain boundary morphology in Cu-Ni-Si-Mn alloys: A machine-learning interatomic potential study
Interfacial energetics computed with machine-learned potentials explain why Mn addition switches grain-boundary precipitates in Cu-Ni-Si alloys from irregular Ni2Si to film-shaped Mn6Ni16Si7.
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Density diversity in training data governs thermodynamic transferability of machine learning interatomic potentials
Density diversity in training data is the key factor for making machine learning interatomic potentials transferable across thermodynamic states, outperforming temperature diversity.
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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.