ArticleFaraday discussions2025
Prediction rigidities for data-driven chemistry.
Article in Faraday discussions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- A universal machine learning model for the electronic density of states.Digital discovery · 2026Article
- Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.Digital discovery · 2025Article
- Uncertainty in the era of machine learning for atomistic modeling.Digital discovery · 2025Review
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Convergence of Body-Orders in Linear Atomic Cluster Expansions.The journal of physical chemistry. A · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (
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.