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Data for Successes and challenges in using machine-learned activation energies in kinetic simulations
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Ismail, Idil, Robertson, Christopher and Habershon, Scott (2022) Data for Successes and challenges in using machine-learned activation energies in kinetic simulations. [Dataset]
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README - Published Version Available under License Creative Commons Attribution 4.0. Download (385b) |
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Data.zip - Published Version Available under License Creative Commons Attribution 4.0. Download (35Kb) |
Official URL: http://wrap.warwick.ac.uk/166748/
Abstract
The prediction of the thermodynamic and kinetic properties of chemical reactions is increasingly being addressed by machine-learning (ML) methods such as artificial neural networks (ANNs). While a number of recent studies have reported success in predicting chemical reaction activation energies, less attention has focused on how the accuracy of ML predictions filter through to predictions of macroscopic observables. Here, we consider the impact of the uncertainty associated with ML prediction of activation energies on observable properties of chemical reaction networks, as given by microkinetics simulations based on ML-predicted reaction rates. After training an ANN to predict activation energies given standard molecular descriptors for reactants and products alone, we performed microkinetics simulations of three different prototypical reaction networks: formamide decomposition, aldol reactions and decomposition of 3-hydroperoxypropanal. We find that the kinetic modelling predictions can be in excellent agreement with corresponding simulations performed with ab initio calculations, but this is dependent on the inherent energetic landscape of the networks. We use these simulations to suggest some guidelines for when ML-based activation energies can be reliable, and when one should take more care in applications to kinetics modelling.
| Item Type: | Dataset | |||||||||
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| Subjects: | Q Science > QD Chemistry T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry Faculty of Science, Engineering and Medicine > Science > Chemistry > Computational and Theoretical Chemistry Centre |
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| Library of Congress Subject Headings (LCSH): | Artificial intelligence, Machine learning, Neural networks (Computer science), Density functionals, Chemical reactions -- Research | |||||||||
| Publisher: | University of Warwick, Department of Chemistry | |||||||||
| Official Date: | 28 June 2022 | |||||||||
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| Status: | Not Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Media of Output (format): | ASCII, .csv | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Copyright Holders: | University of Warwick | |||||||||
| Description: | Each sub-directory contains the data or directions for each of the figures 2-8 in the manuscript. Data are in ASCII text or CSV format. |
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| Date of first compliant deposit: | 28 June 2022 | |||||||||
| Date of first compliant Open Access: | 28 June 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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