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Thiemann, Thiago Resch¨ utzegger, Massimiliano Esposito, Tseden Taddese, Juan D

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

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2026 2 2025 1

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representative citing papers

A Priori Sampling of Transition States with Guided Diffusion

physics.chem-ph · 2026-03-26 · conditional · novelty 8.0

ASTRA reframes transition-state search as guided diffusion inference that samples the isodensity surface between metastable basins and converges to first-order saddles via score differences and physical forces.

Generative Pseudo-Force Fields for Molecular Generation

cs.LG · 2026-05-18 · unverdicted · novelty 7.0

Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.

PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling

cs.LG · 2025-10-05 · unverdicted · novelty 6.0

PAINET proposes an SE(3)-equivariant transformer with physics-inspired attention from energy minimization for 3D dynamics modeling, reporting 4.7-41.5% error reductions on human motion, molecular, and protein benchmarks.

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Showing 3 of 3 citing papers.

  • A Priori Sampling of Transition States with Guided Diffusion physics.chem-ph · 2026-03-26 · conditional · none · ref 44

    ASTRA reframes transition-state search as guided diffusion inference that samples the isodensity surface between metastable basins and converges to first-order saddles via score differences and physical forces.

  • Generative Pseudo-Force Fields for Molecular Generation cs.LG · 2026-05-18 · unverdicted · none · ref 93

    Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.

  • PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling cs.LG · 2025-10-05 · unverdicted · none · ref 12

    PAINET proposes an SE(3)-equivariant transformer with physics-inspired attention from energy minimization for 3D dynamics modeling, reporting 4.7-41.5% error reductions on human motion, molecular, and protein benchmarks.