Automated learning data-driven potential models for spectroscopic characterization of astrophysical interest noble gas-containing NgH2+ molecules

Automated learning data-driven potential models for spectroscopic characterization of astrophysical interest noble gas-containing NgH2+ molecules

The choice of a proper machine learning (ML) algorithm for constructing potential energy surface (PES) models has become a crucial tool in the fields of quantum chemistry and computational modeling. These algorithms offer the ability to make reliable and accurate predictions at a reasonable computat...

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Bibliographic Details
Main Authors: María Judit Montes de Oca-Estévez, Rita Prosmiti
Format: Article
Language:English
Published: Elsevier 2024-06-01
Series:Artificial Intelligence Chemistry
Subjects:
Noble gas hydrides molecules
Theoretical quantum chemistry calculations
Electronic structure calculations
Intermolecular potentials
Machine-learning potential energy surfaces
Molecular spectroscopic constants
Online Access:http://www.sciencedirect.com/science/article/pii/S2949747724000174

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http://www.sciencedirect.com/science/article/pii/S2949747724000174

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