Solvent Selection for Chemical Reactions: Automated Screening Using the SMD Model

An automated procedure calculates solvent effects for a chemical reaction using all 179 solvents available in the SMD solvation model. The reaction of 2-bromoacetophenone with pyridine was used as a test case, and the model correctly predicted reactivity trends for five solvents with available experimental data. The study, authored by Ellen V. Dalessandro, points out that computational screening of large solvent sets is a viable approach for chemical reaction optimization.

Use Cases

• Predicting reaction kinetics and thermodynamics based on calculated solvent effects.
• Screening a large set of 179 solvents to identify optimal candidates for a specific reaction.
• Validating computational models by comparing predicted reactivity trends with experimental data.
• Optimizing chemical reaction conditions by identifying less common but effective solvents like sulfolane.

Strengths

• The dataset is derived from a systematic screening of 179 solvents using the SMD model.
• The methodology was validated against experimental data for five solvents, showing correct prediction of reactivity trends.
• The study identifies a specific, less usual solvent (sulfolane) as optimal for the test reaction.

Limitations

• Row count and data scale are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is focused on a single test reaction (2-bromoacetophenone with pyridine), limiting generalizability.

Provenance

Source

Ellen V. Dalessandro via paperswithcode

Collection Method

Automated computational procedure using the SMD solvation model.