All Articles

From unstructured to structured: opportunities, risks and engineering practices for LLM-based data extraction in chemistry and materials

A closed-loop universal catalyst design workflow ready for AI agents

An energy-efficient scheduling approach for wind-solar-hydrogen systems based on distributed reinforcement learning

AI agents as co-designers of catalysts: a paradigm shift for designing Cu-based single-atom alloy catalysts in CO₂ electroreduction

DigMethpy: an AI-empowered digital catalysis platform for methane pyrolysis molten catalyst design

On-premise AI agents: a future foundation for education, academia, and industry

The rise of the digital materials ecosystem

Robust global optimization of atomic structures via a learning loss-informed on-the-fly firefly algorithm

An integrated energy system scheduling method considering year-round load variations based on deep reinforcement learning

AI agents: opportunity, hype, and the way through

DIVE-to-design: how a multi-agent workflow converts figure-centric literature into an ai-native hydrogen storage discovery engine

StableOx-Cat agent: an AI agent for exploring stable metal oxide electrocatalysts

AI agents for solid electrolytes: opportunities, challenges, and future directions

AI as a catalyst for transforming scientific research: a perspective

From large language models to AI agents in energy materials research: enabling discovery, design, and automation

Knowledge-extractor: a self-evolving scientific framework for hydrogen energy research driven by AI agents

Exploring structures of nanoclusters combining high-dimensional neural network potentials with unsupervised machine learning algorithms

Accelerating multimetallic catalyst discovery with robotics and agentic AI