Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
PulseAugur coverage of Machine Learning Interatomic Potentials as Emerging Tools for Materials Science — every cluster mentioning Machine Learning Interatomic Potentials as Emerging Tools for Materials Science across labs, papers, and developer communities, ranked by signal.
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New agentic system streamlines atomistic simulations for materials discovery
Researchers have developed "El Agente Potente," a novel agentic system designed to streamline atomistic simulations for materials science. This system integrates typed execution graphs with a coding mode, allowing large…
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New MLIP-based method enhances material generation and evaluation
Researchers have introduced a novel approach for generating and evaluating inorganic crystal structures using representations from pretrained Machine-Learning Interatomic Potentials (MLIPs), specifically MACE. They deve…
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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…