Challenges and Opportunities of Pretrained Machine Learning Interatomic Potentials in Heterogeneous Catalysis. [PDF]
Loveday O, Kaźmierczak K, López N.
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A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials. [PDF]
Kim D +6 more
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Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science. [PDF]
Kim J, Cho H, Jeon H, Jung J, Han S.
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A scalable and quantum-accurate foundation model for biomolecular force fields via linearly tensorized quadrangle attention. [PDF]
Su Q +13 more
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Equivariant diffusion solution for inorganic crystal structure determination from powder X-ray diffraction data. [PDF]
Yu D, Zhu Z, Leng F, Zhu Y.
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When Lie Groups Meet Hyperspectral Images: Equivariant Manifold Network for Few-Shot HSI Classification. [PDF]
Ban H +7 more
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Resolving chemical-motif similarity with enhanced atomic structure representations for accurately predicting descriptors at metallic interfaces. [PDF]
Cai C, Wang T.
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Data-Efficient Equivariant NNPs Enable DFT-Accurate Simulations and Implicit Solvation Free Energies. [PDF]
Mutlu E +3 more
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Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces. [PDF]
Bian T, Zhu W, Lei Q, Yin J.
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Equivariant geometric convolutions for dynamical systems on vector and tensor images. [PDF]
Gregory WG +5 more
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