E Marques, S De Gendt, G Pourtois… - Journal of Chemical …, 2023 - ACS Publications
Predicting chemical activation energies is one of the longstanding and important challenges in computational chemistry. Recent advances have shown that machine learning can be …
Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be …
K Jorner, A Tomberg, C Bauer, C Sköld… - Nature Reviews …, 2021 - nature.com
As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until 'big data'applications are reached. These methods no …
M Kayala, P Baldi - Advances in Neural Information …, 2011 - proceedings.neurips.cc
Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Previous approaches are not high-throughput, are not …
Physics-based representations constructed using only atomic positions and nuclear charges (also known as quantum machine learning, QML) allow for the reliable and efficient …
T Stuyver, CW Coley - The Journal of Chemical Physics, 2022 - pubs.aip.org
There is a perceived dichotomy between structure-based and descriptor-based molecular representations used for predictive chemistry tasks. Here, we study the performance …
Quantitative predictions of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer …
Studying organic reaction mechanisms using quantum chemical methods requires from the researcher an extensive knowledge of both organic chemistry and first-principles …
MA Kayala, P Baldi - Journal of chemical information and …, 2012 - ACS Publications
Proposing reasonable mechanisms and predicting the course of chemical reactions is important to the practice of organic chemistry. Approaches to reaction prediction have …