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MLIPs
MLIPs
PulseAugur coverage of MLIPs — every cluster mentioning MLIPs across labs, papers, and developer communities, ranked by signal.
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新框架实现机器学习势能的可扩展、鲁棒主动学习
研究人员开发了一种新的机器学习势能（MLIPs）主动学习框架，解决了可扩展性和鲁棒性挑战。该框架利用力感知神经切线核（NTK）来高效筛选大量的分子结构候选池。该方法在OC20数据集上表现出有效性，实现了低能量和力误差，并在其他基准测试中保持竞争力且鲁棒。
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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…