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
0 records
No records of this kind in this topic yet.
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.
ace