ArticleNature communications2026
Exploring chemistry and catalysis by biasing skewed distributions via deep learning.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Reactivity of Gas-Phase Metal Clusters: Unlocking Mechanisms of Activation and Conversion of Small Molecules.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Exploring chemistry and catalysis by biasing skewed distributions via deep learning.Nature communications · 2026Article
- The seeds of the future are in the present: A blind exploration of metastable states.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The automated discovery of chemical and catalytic reactions remains a major challenge in computational chemistry, particularly in complex systems where conventional methods struggle to identify optimal searching directions. Here, we propose Loxodynamics, a machine-learning-driven approach for reaction exploration via biased molecular dynamics. By leveraging the skewness of local probability distributions, Loxodynamics dynamically determines low-energy barrier directions, efficiently guiding the system toward metastable states. The core of our framework is Skewencoder, an Autoencoder augmented with a skewness-based loss function that extracts reaction coordinates from minimal sampling data. Through iterative sample-and-search cycles, the system adaptively maps the free energy surface, capturing finite-temperature effects critical to complex reactive environments. We validate our method across model potentials, gas-phase reactions (S
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.