arXiv (All)
open access
Learning continuous reaction paths for transition-state prediction
Transition states are defined by reaction pathways, yet most machine-learning methods predict them as isolated geometries. We introduce MARC-TS, a two-stage framework that learns a continuous, endpoint-conditioned path, queries it at any resolution and uses local path context to refine a transition-
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- RD-JEPA: Predictive latent pretraining for few-trajectory transfer across reaction--diffusion equations#1077332
- Squidly harnesses enzyme functional hierarchy and contrastive learning to efficiently predict catalytic residues from sequence.#659568
- TopoHSE-DB: A Topological Materials Database for Generative and Predictive Models#455906
- Heterogeneous Property-Cross-Aware Relation Networks for Molecular Representation Learning#381268
- Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks#1131313
Record · ID 1061162
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive