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.
Harry and Browning, Nicholas J
7 Pith papers cite this work. Polarity classification is still indexing.
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Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
A closed-loop framework jointly optimizes molecular composition and geometry in multi-component systems, demonstrated by a 30% reduction in activation barrier for a Claisen rearrangement via post-hoc validation.
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
MACE-MP-0 is a general-purpose atomistic ML force field trained on public data that enables stable simulations of diverse chemical systems with qualitative and sometimes quantitative accuracy, serving as a starting point for fine-tuning.
Systematic tests show naive fine-tuning excels for single-task accuracy while multihead replay best preserves out-of-distribution robustness in MLIP adaptation.
AiiDA-TrainsPot introduces an automated workflow for training neural-network interatomic potentials via calibrated active learning on carbon allotropes and alloy phase transitions.
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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Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
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Hierarchical generative modeling for the design of multi-component systems
A closed-loop framework jointly optimizes molecular composition and geometry in multi-component systems, demonstrated by a 30% reduction in activation barrier for a Claisen rearrangement via post-hoc validation.
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Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
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A foundation model for atomistic materials chemistry
MACE-MP-0 is a general-purpose atomistic ML force field trained on public data that enables stable simulations of diverse chemical systems with qualitative and sometimes quantitative accuracy, serving as a starting point for fine-tuning.
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Fine-tuning MLIP foundation models: strategies for accuracy and transferability
Systematic tests show naive fine-tuning excels for single-task accuracy while multihead replay best preserves out-of-distribution robustness in MLIP adaptation.
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AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials
AiiDA-TrainsPot introduces an automated workflow for training neural-network interatomic potentials via calibrated active learning on carbon allotropes and alloy phase transitions.