Genetic descriptor search algorithm for predicting hydrogen adsorption free energy of 2D material

Transition metal dichalcogenides (TMDs) have emerged as a promising alternative to noble metals in the field of electrocatalysts for the hydrogen evolution reaction. However, previous attempts using machine learning to predict TMD properties, such as catalytic activity, have been shown to have limit...

Descripción completa

Detalles Bibliográficos
Autores principales: Lee, Jaehwan, Shin, Seokwon, Lee, Jaeho, Han, Young-Kyu, Lee, Woojin, Son, Youngdoo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10404247/
https://www.ncbi.nlm.nih.gov/pubmed/37543706
http://dx.doi.org/10.1038/s41598-023-39696-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10404247/
https://www.ncbi.nlm.nih.gov/pubmed/37543706
http://dx.doi.org/10.1038/s41598-023-39696-0

Ejemplares similares