Extending machine learning beyond interatomic potentials for predicting molecular properties
Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …
chemical processes and materials. ML provides a surrogate model trained on a reference …
[HTML][HTML] Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials
A Omranpour, P Montero De Hijes, J Behler… - The Journal of …, 2024 - pubs.aip.org
As the most important solvent, water has been at the center of interest since the advent of
computer simulations. While early molecular dynamics and Monte Carlo simulations had to …
computer simulations. While early molecular dynamics and Monte Carlo simulations had to …
Probing transferability of intermolecular interactions by their features: a nitro group case study
IV Ananyev, LL Fershtat - Mendeleev Communications, 2024 - Elsevier
Based on the processing of supramolecular environments of nitro group from Cambridge
Structural Database by means of the 'Atoms in Molecules' analysis of promolecular electron …
Structural Database by means of the 'Atoms in Molecules' analysis of promolecular electron …