SciPost Submission Page
Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal
by Maria Harris Rasmussen, Mads Madsen, Jan H. Jensen
This Submission thread is now published as
Submission summary
| Submission information |
| Preprint Link: |
scipost_202109_00010v1
(pdf)
|
| Date accepted: |
2021-09-15 |
| Date submitted: |
2021-09-08 09:24 |
| Submitted by: |
Jensen, Jan H. |
| Submitted to: |
SciPost Chemistry |
| Ontological classification |
| Academic field: |
Chemistry |
| Specialties: |
- Theoretical and Computational Chemistry
|
| Approaches: |
Theoretical, Computational |
Abstract
We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (J. Chem. Theory. Comput. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in about 15-30 minutes using relatively modest computational resources.
