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T. Smidt

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#machine learning Preprint Sep 2026

FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions

Free energy Offering Generative Sampler (FrOGS), a hybrid discrete neural sampler that couples an autoregressive model to a continuous-time Markov chain to be trained jointly under a single shared loss to produce estimates on a common absolute free-energy scale.

Kyucheol Min, E. Hofgard, T. Smidt · 1 citation · ⚡1
Preprint Jul 2026

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

This work constructs an invariant representation of the reciprocal lattice that is independent of primitive cell convention, based on the bispectrum--a descriptor built from spherical harmonic projections of lattice points that could serve more broadly as a geometry-aware target for other crystallographic machine learn...

E. Hofgard, Kyucheol Min, Nofit Segal et al. · 1 citation
Preprint Jul 2026

Reconstructing local environments from concise atomistic representations

This work investigates the inverse problem of recovering atomic structures from local invariant descriptors, and shows that accurate reconstructions can be obtained from remarkably compact descriptors of different correlation orders, each comprising only a few tens of features.

Jigyasa Nigam, T. Phung, Ameya Daigavane et al. · 1 citation
Open access Feb 2026

Machine learning of electronic structure and atomistic properties from the external potential.

This work proposes an operator-centric framework in which the external (nuclear) potential, expressed in an AO basis, serves as the model input and builds hierarchical, body-ordered representations of atomic configurations that closely mirror the principles underlying several popular atom-centered descriptors.

Jigyasa Nigam, T. Smidt, G. Dusson · 2 citations

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