MLIPs
PulseAugur coverage of MLIPs — every cluster mentioning MLIPs across labs, papers, and developer communities, ranked by signal.
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New MoE architectures boost MLIPs performance and interpretability
Researchers have developed new Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for Machine Learning Interatomic Potentials (MLIPs). These models, integrated into the DPA3 framework, demonstra…
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New ontology standardizes machine learning interatomic potentials
Researchers have developed a new ontology, the MLIPs ontology, to standardize the description of machine learning interatomic potentials (MLIPs). This OWL 2 DL ontology aims to address the scattered metadata issue in th…
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New framework enables scalable, robust active learning for MLIPs
Researchers have developed a new active learning framework for machine-learning interatomic potentials (MLIPs) that addresses scalability and robustness challenges. This framework utilizes a force-aware Neural Tangent K…
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AI model learns long-range electrostatics with polarizable atomic multipoles
Researchers have developed a new framework for machine learning interatomic potentials (MLIPs) that addresses the challenge of long-range electrostatics and polarization. This approach uses polarizable atomic multipoles…
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Mixture of Experts framework speeds up atomistic simulations
Researchers have developed a new Mixture-of-Experts (MoE) framework for Machine Learning Interatomic Potentials (MLIPs) to accelerate atomistic simulations. This approach divides simulation domains into regions of varyi…