Chemistry
12 gains0 Traces1 problem
Every measured gain and problem on the record for this topic, newest first.Too few to weigh yet. Updated .
One square per record, darker for stronger evidence:Evidence: 1 high10 moderate2 emerging
13 records so far, too few to weigh yet. Is your study missing? Add it
AI in chemistry
A Trace is one record with both a gain and a problem, so it counts on both sides: 12 records show a gain and 1 shows a problem. 7 context records are in the record list but not counted, because they report no measured gain or problem.
| Evidence | Gain side | Problem side |
|---|---|---|
| HighStrong study design, backed by at least two independent sources | 1 | 0 |
| ModerateA measured study or an official source | 10 | 0 |
| EmergingEarly, small, unreviewed or reported second-hand | 1 | 1 |
By type of evidence: 11 benchmark or lab tests; 1 commentary or narrative review; 1 controlled comparison. How evidence is scored
13 records so far, too few to weigh yet. A written summary appears once a topic has at least 20 counted records, with at least 5 on each side. Is your study missing? Add it
An AI model trained on molecules with up to 8 heavy atoms kept chemical accuracy against reference DFT calculations on organic molecules up to 54 atoms, at force-field computational cost.
An AI-guided literature search picked a light-driven catalyst that made hydrogen peroxide at 1.6 mM per hour, twice the 0.7 mM per hour of a comparison catalyst, reaching 82.3 mM after 72 hours.
In a benchmark study using 608 data points, an adaptive boosting support vector regression model predicted retention times for 61 C1-C12 hydrocarbons across 73 gas chromatography settings, scoring R2 of 0.992-0.993 on validation and testing sets.
A decision tree trained on data from 409 organic chemical processes improved some environmental impact predictions, but uncertainty stayed very high, and scarce inventory data limits automated life cycle assessment.
20 records · page 1 of 2
Researchers built the virtual toxicity network, a deep learning model that combines chemical descriptors, high-throughput assay responses, transcriptomic signatures and the Reactome pathway hierarchy.
Journal of Hazardous Materials
By September 2026, this peer-reviewed synthesis review found AI in organic chemistry shifting from isolated demonstrations to a deployable toolkit covering design, retrosynthesis planning, condition…
This Account from a chemical engineering group reviews how AI and digital tools can support sustainable chemistry across three areas: analysing chemical value chains and planning routes from net-zero feedstocks,…
Researchers built a deep chemometrics framework that reads infrared spectra of olive oil to infer how the olives were irrigated and fertilized.
Researchers compared three ways of feeding data to machine learning models that estimate the thermal decomposition temperature of energetic materials: molecular topological features, features from first-principles…
Researchers synthesized three structurally different bimetallic zeolitic imidazolate framework materials and ran adsorption experiments planned with Latin Hypercube Sampling across environmentally…
Researchers applied transfer learning to deep learning models that predict the environmental impacts of producing chemicals. The models were first pretrained on molecular price data.
Researchers built a detection platform that pairs a microfluidic chip with a deep learning model to identify heavy metal ions and estimate their concentrations.
Published October 3 2026, researchers investigated how P450 enzymes metabolize aniline disinfection byproducts formed during water treatment.
Researchers applied an interpretable AI method combining SISSO symbolic regression with partial-derivative sensitivity analysis to link physicochemical parameters to measured performance of supported…
A peer-reviewed study introduced the Conformal Sampling of Catalytic Processes workflow to model heterogeneous CO2 hydrogenation on copper and nickel.
The concentration of iron ions is a key indicator for assessing water quality. In this study, nitrogen-doped carbon dots (NCDs) were synthesized using a microwave-assisted method with citric acid and…
Machine learning interatomic potentials have become an effective method for exploring complex potential energy surfaces; however, their application to atomic clusters is frequently hindered by the…
Researchers built a machine learning pipeline to predict retention times in capillary gas chromatography, training six algorithms on 608 data points covering 73 instrument settings and 61 C1-C12…
Researchers presented a delta-ML approach for transition metal complexes that uses GFN2-xTB geometry optimizations combined with DFT single-point calculations to produce low-fidelity inputs, which are…
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