Symmetric and antisymmetric kernels for machine learning problems in
quantum physics and chemistry

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Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry

31 March 2021

Frank Noé

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Papers citing "Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry"

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Title
Physically-informed change-point kernels for structural dynamics D. J. Pitchforth M. R. Jones S. Gibson E. Cross 22 0 0 13 Jun 2025
Scalable learning of potentials to predict time-dependent Hartree-Fock dynamics Harish S. Bhat Prachi Gupta Christine M Isborn 61 1 0 08 Aug 2024
Approximating invariant functions with the sorting trick is theoretically justified Wee Chaimanowong Ying Zhu 64 0 0 04 Mar 2024
$Uniform $\mathcal{C}^k$ Approximation of $G$-Invariant and Antisymmetric Functions, Embedding Dimensions, and Polynomial Representations$ Uniform $\mathcal{C}^k$ Approximation of $G$ -Invariant and Antisymmetric Functions, Embedding Dimensions, and Polynomial Representations Soumya Ganguly Khoa Tran Rahul Sarkar 92 0 0 02 Mar 2024
Smooth, exact rotational symmetrization for deep learning on point clouds Sergey Pozdnyakov Michele Ceriotti 3DPC 100 30 0 30 May 2023
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields Niklas Schmitz Klaus-Robert Muller Stefan Chmiela AI4CE 71 11 0 25 Aug 2022