Computational chemistry
ML plus transition states predict SNAr barriers
Open access · cc by · source: Europe PMC
A Gaussian-process model that includes SNAr transition-state features predicts experimental activation energies with 0.77 kcal mol−1 MAE, beating raw DFT.
How this study connects
Role on claims
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Evidence for the claim as stated.
A Gaussian-process model that includes SNAr transition-state features predicts experimental activation energies with 0.77 kcal mol−1 MAE, beating raw DFT.
Evidence for the claim as stated.
ML trained on DFT descriptors can beat raw DFT on a held-out SNAr set and still depend on being able to compute the transition state. A Gaussian-process model reached R² = 0.93 and MAE = 0.77 kcal mol⁻¹ versus raw DFT MAE 2.93; selectivity top-1 accuracy was 86%. Acid/base catalysis and other reaction classes were not in the model; TS geometry remains the bottleneck.
Evidence for the claim as stated.
These papers do not agree on what DFT is for. One trains a statistical model to correct DFT barriers; one trains a net to emulate DFT spin states; others use a single-point or periodic calculation to rationalise a crystal, a gas-phase ion, or a ligand redox event. Quoting a kcal mol⁻¹ figure without saying whether it is raw DFT, a GP correction, or an NNP barrier mixes those jobs.
Evidence for the claim as stated.
Open questions
Tensions this paper is part of
From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.
These papers do not agree on what DFT is for. One trains a statistical model to correct DFT barriers; one trains a net to emulate DFT spin states; others use a single-point or periodic calculation to rationalise a crystal, a gas-phase ion, or a ligand redox event. Quoting a kcal mol⁻¹ figure without saying whether it is raw DFT, a GP correction, or an NNP barrier mixes those jobs.
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
Structured fields used in claim comparison tables when every cited study has a complete layer.
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 ().
Key findings
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.
Methodology
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.
Limitations
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.
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