ReviewReviews in chemical engineering2025
Machine learning applications for thermochemical and kinetic property prediction.
Review in Reviews in chemical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next.ACS engineering Au · 2026Review
- Integrating Artificial Intelligence into Circular Strategies for Plastic Recycling and Upcycling.Polymers · 2026Review
- Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
- NN-VRCTST: Neural Network Potentials Meet Variable Reaction Coordinate Transition State Theory for the Rate Constant Determination of Barrierless Reactions.Journal of chemical theory and computation · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detailed kinetic models play a crucial role in comprehending and enhancing chemical processes. A cornerstone of these models is accurate thermodynamic and kinetic properties, ensuring fundamental insights into the processes they describe. The prediction of these thermochemical and kinetic properties presents an opportunity for machine learning, given the challenges associated with their experimental or quantum chemical determination. This study reviews recent advancements in predicting thermochemical and kinetic properties for gas-phase, liquid-phase, and catalytic processes within kinetic modeling. We assess the state-of-the-art of machine learning in property prediction, focusing on three core aspects: data, representation, and model. Moreover, emphasis is placed on machine learning techniques to efficiently utilize available data, thereby enhancing model performance. Finally, we pinpoint the lack of high-quality data as a key obstacle in applying machine learning to detailed kinetic models. Accordingly, the generation of large new datasets and further development of data-efficient machine learning techniques are identified as pivotal steps in advancing machine learning's role in kinetic modeling.
Indexed as
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.