Latent Flows Model Reaction Trajectories
๐Ÿ“„#research#latentrxnflow#v1Stalecollected in 19h

Latent Flows Model Reaction Trajectories

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๐Ÿ“„Read original on ArXiv AI

โšก 30-Second TL;DR

What changed

No mechanistic supervision needed

Why it matters

Enhances transparency and trustworthiness in reaction prediction for discovery workflows. Prioritizes predictable outcomes, reducing validation needs.

What to do next

Prioritize whether this update affects your current workflow this week.

Who should care:Researchers & Academics

LatentRxnFlow predicts reactions as continuous latent trajectories via Conditional Flow Matching from reactant-product pairs. Offers SOTA USPTO accuracy with trajectory diagnostics and uncertainty estimation. Enables error mitigation and reliable predictions.

Key Points

  • 1.No mechanistic supervision needed
  • 2.Geometric uncertainty signals
  • 3.Gated inference fixes failure modes

Impact Analysis

Enhances transparency and trustworthiness in reaction prediction for discovery workflows. Prioritizes predictable outcomes, reducing validation needs.

Technical Details

Time-dependent dynamics in latent space anchored at products. Spectral analysis localizes errors without discrete steps.

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