Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …

Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Microwave absorption materials (MAMs) are originally developed for military purposes, but
have since evolved into versatile materials with promising applications in modern …

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

Adsorption energy (AE) of reactive intermediate is currently the most important descriptor for
electrochemical reactions (eg, water electrolysis, hydrogen fuel cell, electrochemical …

Lithium‐ion batteries (LIBs) are vital energy‐storage devices in modern society. However,
the performance and cost are still not satisfactory in terms of energy density, power density …

How can firms establish competitive advantages using artificial intelligence (AI)? Although AI
is beginning to permeate business activities, our understanding of how AI can be used to …

Catalyst discovery and optimization is key to solving many societal and energy challenges
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …

The development of materials is one of the driving forces to accelerate modern scientific
progress and technological innovation. Machine learning (ML) technology is rapidly …