AI-Driven Rational Design of Solid-State Electrolytes

Large Language Models in Nanoparticles Research: Methods, Applications, Impacts, and Future Directions

Hybrid Physics-Machine Learning Framework for Multi-Objective Design Optimisation of Carbon/Glass Hybrid Composite Laminates under Bending

From Materials Database to Materials Bank: Assetizing Data for AI-Driven Materials Innovation

Multimodal Literature Mining for Autonomous Hydrogen Storage Materials Discovery

Advances in Machine Learning Potentials for Mineral Physics at High Pressure and High Temperature

Collaborative AI Enhances Image Understanding in Materials Science

Driving Catalytic Innovation: The Development and Application of Machine Learning Force Fields

Let Materials Science Data Learn to Reason

Supervised Machine Learning Assisted Development of Hybrid Solvation Model for Simulating Graphene-Water Interface

Data Speaks: LLM-Enabled Evidence Auditing Resets the “Migration Barrier” Playbook for Solid-State Electrolytes