Quantum mechanical methods like density functional theory (DFT) are used with great success alongside efficient search algorithms for studying kinetics of reactive systems …
Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures …
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 …
Y Yang, MS Eldred, J Zádor… - Journal of Chemical …, 2023 - ACS Publications
This paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions …
E Nieves, R Dandekar, C Rackauckas - Frontiers in Systems Biology, 2024 - frontiersin.org
The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner …
T Petrov, D Repin - arXiv preprint arXiv:2002.01889, 2020 - arxiv.org
Predicting stochastic cellular dynamics as emerging from the mechanistic models of molecular interactions is a long-standing challenge in systems biology: low-level chemical …
R David, M de la Puente, A Gomez, O Anton… - arXiv preprint arXiv …, 2024 - arxiv.org
The emergence of artificial intelligence has profoundly impacted computational chemistry, particularly through machine-learned potentials (MLPs), which offer a balance of accuracy …
The last decade has seen remarkable advances in speech, image, and language recognition tools that have been made available to the public through computer and mobile …