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Related Articles from SNS
Prolate spheroidal wave functions enable fast and exponent-aware long-range machine learning interatomic potentials
arXiv:2606.06617v1 Announce Type: cross Abstract: Long-range interactions such as electrostatics and dispersion remain a central bottleneck for machine learning interatomic potentials (MLIPs), especially in ionic, polar and interfacial systems. Ewald-based reciprocal-space mechanisms provide a physically grounded route for capturing these nonlocal effects, but often require dense Fourier grids and can become memory-limited at scale.
Prolate spheroidal wave functions enable fast and exponent-aware long-range machine learning interatomic potentials
arXiv:2606.06617v1 Announce Type: new Abstract: Long-range interactions such as electrostatics and dispersion remain a central bottleneck for machine learning interatomic potentials (MLIPs), especially in ionic, polar and interfacial systems. Ewald-based reciprocal-space mechanisms provide a physically grounded route for capturing these nonlocal effects, but often require dense Fourier grids and can become memory-limited at scale. This problem is particularly pronounced in molecular...