$Δ^2$ machine learning for reaction property prediction
Qiyuan Zhao, Dylan M. Anstine, Olexandr Isayev, Brett M. Savoie
Highlight
Newly developed Δ 2 -learning models enable state-of-the-art accuracy in predicting the properties of chemical reactions.
Abstract
Newly developed Δ 2 -learning models enable state-of-the-art accuracy in predicting the properties of chemical reactions.
Keywords
Cite This Paper
@article{Zhao2023delta,
author = {Zhao, Qiyuan and Anstine, Dylan M. and Isayev, Olexandr and Savoie, Brett M.},
title = {$Δ^2$ machine learning for reaction property prediction},
year = {2023},
journal = {Chem. Sci.},
volume = {14},
number = {46},
pages = {13392--13401},
doi = {10.1039/d3sc02408c},
keywords = {delta learning, reaction prediction},
researchAreas = {reactions-reactivity},
highlight = {Newly developed Δ 2 -learning models enable state-of-the-art accuracy in predicting the properties of chemical reactions.}
} Copied to clipboard!
Related Research Areas
Related Publications
Teaching a neural network to attach and detach electrons from molecules
Nature Communications, 12 (2021)
Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations.
Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions
(2024)
Ring strain energy (RSE) is crucial for understanding molecular reactivity.
Uncertainty-Aware Yield Prediction with Multimodal Molecular Features
, 38, 8274–8282 (2024)
Predicting chemical reaction yields is pivotal for efficient chemical synthesis, an area that focuses on the creation of novel compounds for diverse uses.
The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions
(2023)
Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis, but current models have failed to generalize to large literature datasets.
The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions
Chem. Sci., 14, 10835–10846 (2023)
A sensitive model captures the reactivity cliffs but overfit to yield outliers.