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20102026
most citedE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

1.9k citations · 4k across the 54 of their papers we have counts for

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Showing 2026 · physics.comp-phShow all

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physics.comp-ph2026

Reconstructing local environments from concise atomistic representations

Jigyasa Nigam, Tuong Phung, Ameya Daigavane +2

Symmetry-based representations of local atomic structure, such as the power spectrum or bispectrum, are routinely used to characterize the structural diversity of datasets and as i…

physics.comp-ph2026

Learning Lattice Parameters from Powder X-Ray Diffraction Data Using Invariants

Elyssa Hofgard, Kyucheol Min, Nofit Segal +8

We present a machine learning (ML) method to determine unit cell parameters from powder X-Ray diffraction (XRD) data using a novel invariant lattice representation. In ML, the data…