Mace
PulseAugur coverage of Mace — every cluster mentioning Mace across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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New ML dataset accelerates catalysis research in 2D MXenes
Researchers have developed a new benchmark dataset and machine learning models to accelerate the study of catalysis in 2D MXenes. By combining density functional theory calculations with machine learning interatomic pot…
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New MLIP methods improve accuracy and automate research
Researchers are developing advanced machine learning interatomic potentials (MLIPs) to improve atomistic simulations. New methods like Stein Kernelized Molecular Dynamics (SKMD) enhance data acquisition for active learn…
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New kernels from pretrained MACE potentials improve active learning for MLIPs
Researchers have developed a new method for active learning in machine learning interatomic potentials (MLIPs) by utilizing pretrained model representations. This approach leverages the latent space of a pretrained MACE…
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Machine learning potentials struggle to predict silica glass structure
Researchers have investigated the limitations of machine learning potentials in accurately predicting the medium-range order of silica glass. Using neutron and X-ray diffraction alongside molecular dynamics, they found …