Imagem da capa

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

Na minha lista:
Publicado no:NPJ Computational Materials vol. 11, no. 1 (2025), p. 196
Autor principal: Hörmann, Lukas
Outros Autores: Stark, Wojciech G., Maurer, Reinhard J.
Publicado em:
Nature Publishing Group
Assuntos:
Machine learning
Simulation
Science
Genetic algorithms
Optimization
Molecular beam epitaxy
Computer applications
Electronic structure
Two dimensional materials
Interfaces
Learning algorithms
Acesso em linha:Citation/Abstract
Full Text
Full Text - PDF
Tags: Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!
Resumo: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
Fonte:Health & Medical Collection