Machine learning-inspired battery material innovation

MF Ng, Y Sun, ZW Seh - Energy Advances, 2023 - pubs.rsc.org
Machine learning (ML) techniques have been a powerful tool responsible for many new
discoveries in materials science in recent years. In the field of energy storage materials …

Discovery of Efficient Visible‐light Driven Oxygen Evolution Photocatalysts: Automated High‐Throughput Computational Screening of MA2Z4

C Lin, X Feng, D Legut, X Liu, ZW Seh… - Advanced Functional …, 2022 - Wiley Online Library
Photocatalytic oxygen evolution reaction (OER) by 2D semiconductors is a promising
strategy for efficient energy conversion. The newly discovered 2D semiconductors MA2Z4 …

Poly (ether imide) porous membrane developed by a scalable method for high-performance lithium–sulfur batteries: combined theoretical and experimental study

W Raza, A Hussain, A Mehmood, Y Deng… - … Applied Materials & …, 2022 - ACS Publications
Lithium–sulfur (Li–S) batteries are one of the emerging candidates for energy storage
systems due to their high theoretical energy density and the abundance/nontoxicity/low cost …

A systematic study on the metallophilicity of ordered five-atomic-layer MXenes using high-throughput automated workflow and machine learning

X Feng, R Dong, Y Li, X Liu, C Lin, T Wang… - Energy Storage …, 2023 - Elsevier
Dendrite growth is an important issue hindering practical applications of various kinds of
metal (eg Li, Na, K, Mg, Ca, Fe, Zn, and Al) batteries. To tackle this problem, an effective …

DEST: A Simplified Model and Automated Tool for Loss of Lithium Inventory and Loss of Active Material Estimation in Li‐ion Batteries

FJ Méndez‐Corbacho, D Nieto‐Castro… - …, 2024 - Wiley Online Library
Li‐ion batteries are attracting an increasing attention due to the process of electrification
involving different industrial sectors. Many efforts are dedicated to improving battery …