Imagen de Portada

Machine learning and data-driven methods in computational surface and interface science

Guardado en:
Publicado en:NPJ Computational Materials vol. 11, no. 1 (2025), p. 196
Autor principal: Hörmann, Lukas
Otros Autores: Stark, Wojciech G., Maurer, Reinhard J.
Publicado:
Nature Publishing Group
Materias:
Machine learning
Simulation
Science
Genetic algorithms
Optimization
Molecular beam epitaxy
Computer applications
Electronic structure
Two dimensional materials
Interfaces
Learning algorithms
Acceso en línea:Citation/Abstract
Full Text
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:Machine learning and data-driven methods have started to transform the study of surfaces and interfaces. Here, we review how data-driven methods and machine learning approaches complement simulation workflows and contribute towards tackling grand challenges in computational surface science from 2D materials to interface engineering and electrocatalysis. Challenges remain, including the scarcity of large datasets and the need for more electronic structure methods for interfaces.
ISSN:2057-3960
DOI:10.1038/s41524-025-01691-6
Fuente:Health & Medical Collection