AI can retrieve chemical information, predict properties, generate candidates, or prioritize experiments, but generation does not establish synthesizability, stability, safety, or target performance. Every workflow must define composition, structure, environment, properties, and validation level.
When this workflow is useful
- Screening known chemical or materials spaces
- Proposing structures under explicit property constraints
- Creating a traceable priority queue for computation and experiment
A practical sequence
- 01
Define objectives and constraints
Specify properties, units, conditions, acceptable ranges, composition, cost, safety, availability, and synthesizability. Do not collapse objectives into an unexplained score.
- 02
Assemble baseline data
Collect structures, properties, and conditions from authoritative databases and primary literature. Normalize identifiers and units and distinguish measured, calculated, and predicted values.
- 03
Choose representations and validation
Select graphs, strings, crystals, or descriptors appropriate to the task. Define temporal, scaffold, structural, or external tests to limit leakage.
- 04
Generate, rank, and validate
Check chemical validity, stability, duplication, applicability, and synthesizability. Record models and thresholds, then challenge candidates through independent computation and experiment, retaining failures.
Quality-control questions
- Are property definitions, units, and conditions consistent?
- Could similarity or leakage connect training and test sets?
- Were candidates checked for validity, stability, and safety?
- Were consequential predictions independently tested?