ML plus transition states predict SNAr barriers
A Gaussian-process model that includes SNAr transition-state features predicts experimental activation energies with 0.77 kcal mol−1 MAE, beating raw DFT.
Source
Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies
Study at a glance
- Design
- Computational / modelling — DFT/physical-organic descriptors with GPR for SNAr barriers and selectivity
- N
- ML on reaction barriers/selectivity; learning curves emphasize 50–150 training samples — no single cohort N
- Population
- Nucleophilic aromatic substitution reactions (including patent-selectivity cases)
- Outcome
- Predicted activation free energies and regio-/chemoselectivity accuracy
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Inspect source passages
ML plus transition states predict SNAr barriers
These passages occur verbatim in the stored paper text. They support the points listed below; they do not verify every statement in the explanation.
GPR MAE is 0.77 kcal mol−1 on the external test set, below 1 kcal mol−1 chemical accuracy.
The chosen Gaussian Process Regression model achieved a mean absolute error of only 0.77 kcal mol −1 on the external test set, well below the targeted chemical accuracy of 1 kcal mol −1 .
Raw DFT barriers have MAE 2.93 kcal mol−1.
In comparison, the raw DFT barriers Δ G ‡ DFT show a high MAE of 2.93 kcal mol −1 and have the same predictive value as just guessing the mean of the training dataset ().
What they did
Authors combined physical-organic and DFT transition-state descriptors for nucleophilic aromatic substitutions, trained several regressors, and tested regio-/chemoselectivity on patent reactions plus learning curves versus sample size.
What they found
GPR (Matern 3/2) reaches R2 = 0.93 and MAE = 0.77 kcal mol−1 on a held-out set, below 1 kcal mol−1 chemical accuracy. Raw DFT MAE is 2.93. Selectivity top-1 accuracy is 86%. Hybrid models help most with 50–150 samples.
The limits
What it doesn't show
Acid/base catalysis and other reaction classes are not yet in the model; TS geometry still needs reliable computation, which the authors call the bottleneck.
Key terms
- SNAr
- Nucleophilic aromatic substitution, the prototype reaction modeled here.
- Chemical accuracy
- About 1 kcal mol−1 error, the target for useful barrier prediction.
- GPR
- Gaussian process regression; Matern 3/2 kernel was the best model.
- Hybrid model
- ML that mixes mechanistic TS features with traditional QSRR descriptors.
- QSRR
- Quantitative structure–reactivity relationship using physical-organic features.
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Quiz yourself
Best test MAE for SNAr Ea is:
Common questions
How accurate is the best model?
MAE 0.77 kcal mol−1 on the external test set.
How bad is uncorrected DFT?
MAE 2.93 kcal mol−1, no better than guessing the mean.
Can it pick the right site?
86% top-1 regio/chemoselectivity on patent data.
When to use hybrid vs DFT-only?
Hybrid shines around 50–150 labeled reactions.
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