MLIPs
PulseAugur coverage of MLIPs — every cluster mentioning MLIPs across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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AI distillation creates faster MLIPs for battery electrolyte simulation
Researchers have developed a knowledge distillation technique to create faster, more accurate machine learning interatomic potentials (MLIPs) for simulating liquid electrolytes. These "student" models, distilled from la…
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New framework CrystalREPA enhances crystal generative models with MLIP priors
Researchers have developed CrystalREPA, a framework designed to improve crystal generative models by transferring physical stability priors from universal Machine Learning Interatomic Potentials (MLIPs). This method ali…
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MLIPS method boosts LLM efficiency with shared attention pools
Researchers have developed MLIPS (Multi-Layer Indexer Pool Sharing), a novel method designed to improve the efficiency of sparse attention mechanisms in large language models. This training-free approach allows subseque…
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New ML models improve electronic ground-state prediction with physics-aligned training
Researchers have developed new machine learning models for electronic ground-state prediction that significantly improve generalization capabilities beyond current methods. By incorporating physical constraints derived …
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BranchIP framework accelerates MLIPs with adaptive computation
Researchers have introduced BranchIP, a novel framework designed to enhance the efficiency of machine learning interatomic potentials (MLIPs). This new model employs adaptive tensor product computation, a technique that…
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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…