ArticleJournal of the American Chemical Society2024
Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon.
Article in Journal of the American Chemical Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Toward Predictive Theory in Single-Atom Catalysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials.Digital discovery · 2026Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.Digital discovery · 2025Article
- PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems.Journal of chemical theory and computation · 2025Article
- Preparation and Gas-Sensitive Properties of Square-Star-Shaped Leaf-Like BiVONanomaterials (Basel, Switzerland) · 2025Article
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
- Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide.Angewandte Chemie (International ed. in English) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and selecting the one with the closest match to experiment. However, this inefficient process is not guaranteed to succeed. We introduce a general method to combine atomistic machine learning (ML) with experimental observables that produces atomistic structures compatible with experiment
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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.