Machine Learning Descriptors for Data‐Driven Catalysis Study

Machine Learning Descriptors for Data‐Driven Catalysis Study

Abstract Traditional trial‐and‐error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better‐performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predic...

Full description

Bibliographic Details
Main Authors: Li‐Hui Mou, TianTian Han, Pieter E. S. Smith, Edward Sharman, Jun Jiang
Format: Article
Language:English
Published: Wiley 2023-08-01
Series:Advanced Science
Subjects:
catalytic descriptors
heterogeneous catalysis
high‐throughput experiments
machine learning
theoretical simulations
Online Access:https://doi.org/10.1002/advs.202301020

Internet

https://doi.org/10.1002/advs.202301020

Similar Items