Reactants, Products, and Transition States of Elementary Chemical Reactions Based on Quantum Chemistry

Grambow, Colin A.; Pattanaik, Lagnajit; Green, William

doi:10.26434/chemrxiv.11400240.v2

Cited by 15 publications

(19 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We envision that this problem will be solved in the near future by deep learning approaches that can predict both TS geometries 58 and DFT-computed barriers 26 based on large, publicly available datasets. 59,60 In the end, machine learning for reaction prediction needs to reproduce experiment, and transfer learning will probably be key to utilizing small high-quality kinetic datasets together with large amounts of computationally generated data. Regardless of their construction, accurate reaction prediction models will be key components of accelerated route design, reaction optimization and process design enabling the delivery of medicines to patients faster and with reduced costs.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies

Jorner

Brinck²,

Norrby³

et al. 2020

Preprint

View full text Add to dashboard Cite

Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers with a mean absolute error of 0.77 kcal/mol for an external test set. The model was further validated on regio- and chemoselectivity prediction on patent reaction data and achieved a competitive top-1 accuracy of 86%, despite not being trained explicitly for this task. Importantly, the model gives error bars for its predictions that can be used for risk assessment by the end user. Hybrid models emerge as the preferred alternative for accurate reaction prediction in the very common low-data situation where only 100–150 rate constants are available for a reaction class. With recent advances in deep learning for quickly predicting barriers and transition state geometries from density functional theory, we envision that hybrid models will soon become a standard alternative to complement current machine learning approaches based on ground-state physical organic descriptors or structural information such as molecular graphs or fingerprints.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies

Jorner

Brinck²,

Norrby³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…We also justify our choice of level of theory by noting that similar levels of theory have previously been used to generate datasets used to study reactivity. In particular, Grambow et al 74 recently used the ωB97X-D3 density functional 75 (which is closely related to ωB97X-V and differs primarily in the choice of dispersion correction) and the def2-TZVP basis set (which is part of the same family as def2-TZVPPD but contains no diffuse functions and fewer polarization functions) to create a dataset of over 12,000 organic reactions (including optimized reactants, products, and transition states) in vacuum. The solution-phase charged and radical organometallic chemistry involved in SEI formation is more complex than the gas-phase organic reactions considered by Grambow et al, necessitating both the inclusion of an implicit solvent model and the use of a larger basis set including diffuse functions.…”

Section: Level Of Theorymentioning

confidence: 99%

Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species

Spotte-Smith

Blau²,

Xie³

et al. 2021

Preprint

View full text Add to dashboard Cite

Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte(LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural,thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties.

show abstract

“…Q-Chem output files, extracted SMILES, activation energies, and enthalpies of formation are available for 16,452 B97-D3/def2-mSVP reactions and for 12,001 ωB97X-D3/def2-TZVP reactions 40 . The raw log files are stored in two compressed archive files, b97d3.tar.gz and wb97xd3.tar.gz for B97-D3/def2-mSVP and ωB97X-D3/def2-TZVP data, respectively.…”

Section: Data Recordsmentioning

confidence: 99%

Reactants, Products, and Transition States of Elementary Chemical Reactions Based on Quantum Chemistry

Grambow¹,

Pattanaik²,

Green³

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Reaction times, activation energies, branching ratios, yields, and many other quantitative attributes are important for precise organic syntheses and generating detailed reaction mechanisms. Often, it would be useful to be able to classify proposed reactions as fast or slow. However, quantitative chemical reaction data, especially for atom-mapped reactions, are difficult to find in existing databases. Therefore, we used automated potential energy surface exploration to generate 12,000 organic reactions involving H, C, N, and O atoms calculated at the ωB97X-D3/def2-TZVP quantum chemistry level. We report the results of geometry optimizations and frequency calculations for reactants, products, and transition states of all reactions. Additionally, we extracted atom-mapped reaction SMILES, activation energies, and enthalpies of reaction. We believe that this data will accelerate progress in automated methods for organic synthesis and reaction mechanism generation---for example, by enabling the development of novel machine learning models for quantitative reaction prediction.

show abstract

Reactants, Products, and Transition States of Elementary Chemical Reactions Based on Quantum Chemistry

Cited by 15 publications

References 32 publications

Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies

Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies

Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species

Reactants, Products, and Transition States of Elementary Chemical Reactions Based on Quantum Chemistry

Contact Info

Product

Resources

About