Machine learning: a new paradigm in computational electrocatalysis
Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms
at an atomic level, and uncovering scientific insights lie at the center of the development of …
at an atomic level, and uncovering scientific insights lie at the center of the development of …
[HTML][HTML] Unlocking the potential: Machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
Oxygen evolution reaction at the Mo/W-doped bismuth vanadate surface: Assessing the dopant role by DFT calculations
The first-principles investigation of M-doped BiVO 4-based materials (M= Mo, W) provides a
comprehensive understanding of the dopant role in enhancing the photocatalytic properties …
comprehensive understanding of the dopant role in enhancing the photocatalytic properties …
Machine learning utilized for the development of proton exchange membrane electrolyzers
Proton exchange membrane water electrolyzers (PEMWEs) have great potential as energy
conversion devices for storing renewable electricity into hydrogen energy. However, their …
conversion devices for storing renewable electricity into hydrogen energy. However, their …
Research progress on graphite-derived materials for electrocatalysis in energy conversion and storage
S He, M Wu, S Li, Z Jiang, H Hong, SG Cloutier… - Molecules, 2022 - mdpi.com
High-performance electrocatalysts are critical to support emerging electrochemical energy
storage and conversion technologies. Graphite-derived materials, including fullerenes …
storage and conversion technologies. Graphite-derived materials, including fullerenes …
Evaluation of polymer electrolyte membrane electrolysis by explainable machine learning, optimum classification model, and active learning
ME Günay, NA Tapan - Journal of Applied Electrochemistry, 2023 - Springer
In this work, a database of 789 experimental points extracted from 30 academic publications
was used. The primary objective was to use novel machine-learning techniques to …
was used. The primary objective was to use novel machine-learning techniques to …
Machine learning applications on proton exchange membrane water electrolyzers: A component-level overview
Abstract Machine Learning (ML) has emerged as a pivotal force in enhancing Proton
Exchange Membrane Water Electrolyzer (PEMWE) devices. These devices are critical for …
Exchange Membrane Water Electrolyzer (PEMWE) devices. These devices are critical for …
Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism
Molecular learning is pivotal in many real-world applications, such as drug discovery.
Supervised learning requires heavy human annotation, which is particularly challenging for …
Supervised learning requires heavy human annotation, which is particularly challenging for …
Machine Learning Utilized for the Development of Proton Exchange Membrane Electrolyzers
Y Chen, Z Rui, K Hua, Y Wu, X Li, X Duan, X Wang… - papers.ssrn.com
The proton exchange membrane water electrolyzers (PEMWEs) have great potential as
energy conversion devices for storing renewable electricity into hydrogen energy. However …
energy conversion devices for storing renewable electricity into hydrogen energy. However …