ReviewChemical science2026
The agentic age of predictive chemical kinetics.
Review in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Flow Modeling, Reactor Engineering, and Process Intensification: From Insight to Performance.ACS engineering Au · 2026Review
- Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next.ACS engineering Au · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predictive chemical kinetic modeling is foundational to areas ranging from energy and environmental science to pharmaceuticals and advanced materials. While significant progress has been made in automating individual steps, the development of a complete predictive model remains a human-intensive effort to orchestrate existing software tools and revise models. This perspective outlines a practical path to improved chemical kinetic model development using agentic AI. A dual-lane architecture is introduced: a fast execution lane handles mechanism generation and parameter refinement, while a deliberative agentic lane plans, refines, and revises while executing experiments and computations. The proposed outcome is a robust pathway toward decision-grade models. Humans remain central: researchers set objectives and priors, approve high-impact actions, and adjudicate new chemical insights. Creativity, complex judgment, and strategic thinking remain in the human domain. Ultimately, this approach aims to accelerate trustworthy, transparent, decision-grade model development.
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.