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New SITA method enables efficient molecular sampling

By PulseAugur Editorial · [4 sources] ·

Researchers have developed a new method called Scalable Inference-Time Annealing (SITA) to efficiently sample molecular Boltzmann distributions. SITA utilizes flow-based models with energy-based surrogates to estimate importance weights, bypassing the need for computationally expensive divergence calculations. This approach demonstrates state-of-the-art performance on molecular systems like Alanine Dipeptide and Tripeptide, offering a more tractable solution for complex simulations. AI

IMPACT Introduces a more efficient method for molecular simulation, potentially accelerating drug discovery and materials science research.

RANK_REASON The cluster contains two arXiv papers detailing new computational methods for sampling molecular distributions.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New SITA method enables efficient molecular sampling

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The cluster contains two arXiv papers detailing new computational methods for sampling molecular distributions.
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COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · Daniel Pe\~naherrera, Rishal Aggarwal, David Ryan Koes ·

    Scalable Inference-Time Annealing with Surrogate Likelihood Estimators

    arXiv:2605.31498v1 Announce Type: new Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampl…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Scalable Inference-Time Annealing with Surrogate Likelihood Estimators

    Scalable inference-time annealing method uses flow-based models with energy-based surrogates to efficiently sample Boltzmann distributions without costly divergence calculations.

  3. arXiv cs.LG TIER_1 English(EN) · David Ryan Koes ·

    Scalable Inference-Time Annealing with Surrogate Likelihood Estimators

    A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampling techniques by eliminating the computational …

  4. arXiv stat.ML TIER_1 English(EN) · Yiyang Fan, Soumyakanti Pan, Tyler H. McCormick ·

    Rashomon-Seeded Annealing for Robust Bayesian Inference in Factorial Designs

    arXiv:2606.02589v1 Announce Type: cross Abstract: Integrating over model uncertainty in factorial designs via Bayesian model averaging is hindered by the combinatorial explosion of interpretable interaction effects, often yielding a multimodal posterior, where standard Markov cha…

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