ReviewPrecision chemistry2024
Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis.
Review in Precision chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- ResNet-KAN for Small-Sample Chemical Formulation Prediction: Hybrid Architecture and Interpretability Analysis.Precision chemistry · 2026Article
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Smarter Data: Rethinking Data Generation for Machine Learning Potentials in Heterogeneous Catalysis.JACS Au · 2026Review
- Theoretical Perspectives of Precision Chemistry.Precision chemistry · 2026Review
- Review
- Challenges and Opportunities of Pretrained Machine Learning Interatomic Potentials in Heterogeneous Catalysis.ACS catalysis · 2026Review
- How to Efficiently Design 2D Materials for Electrochemical Applications Using Machine Learning.Precision chemistry · 2026Article
- Fast and accurate prediction of adsorption energy of AgPd nanoalloys by deep learning potentials and neural networks.Scientific reports · 2026Article
- Modeling the potential energy surface by force fields for heterogeneous catalysis: classification, applications, and challenges.Chemical science · 2025Review
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
- Article
- Artificial-Intelligence Driven Precision Chemistry.Precision chemistry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This Perspective explores the integration of machine learning potentials (MLPs) in the research of heterogeneous catalysis, focusing on their role in identifying
Identifiers
What OpenQuestion holds
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.