ArticleJournal of chemical theory and computation2023
Robustness of Local Predictions in Atomistic Machine Learning Models.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A universal machine learning model for the electronic density of states.Digital discovery · 2026Article
- Unprecedented robustness of physics-informed atomic energy models at and beyond room temperature.Communications chemistry · 2026Article
- The Zintl-Klemm Concept in the Amorphous State: A Case Study of Na-P Battery Anodes.Angewandte Chemie (International ed. in English) · 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
- Interpolating numerically exact many-body wave functions for accelerated molecular dynamics.Nature communications · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.Chemical science · 2025Article
- Analyzing Atomic Interactions in Molecules as Learned by Neural Networks.Journal of chemical theory and computation · 2025Article
- Prediction rigidities for data-driven chemistry.Faraday discussions · 2025Article
- Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning.Angewandte Chemie (International ed. in English) · 2024Article
- SPAJournal of chemical theory and computation · 2024Article
- Mechanism of Charge Transport in Lithium Thiophosphate.Chemistry of materials : a publication of the American Chemical Society · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost and also allows for the identification and posthoc interpretation of contributions from individual chemical environments and motifs to complicated macroscopic properties. However, even though practical justifications exist for the local decomposition, only the global quantity is rigorously defined. Thus, when the atom-centered contributions are used, their sensitivity to the training strategy or the model architecture should be carefully considered. To this end, we introduce a quantitative metric, which we call the local prediction rigidity (LPR), that allows one to assess how robust the locally decomposed predictions of ML models are. We investigate the dependence of the LPR on the aspects of model training, particularly the composition of training data set, for a range of different problems from simple toy models to real chemical systems. We present strategies to systematically enhance the LPR, which can be used to improve the robustness, interpretability, and transferability of atomistic ML models.
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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.