Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
From large language models to AI agents in energy materials research: enabling discovery, design, and automation
0
Zitationen
8
Autoren
2025
Jahr
Abstract
Fragmented knowledge and slow experimental iteration constrain the discovery of energy materials. We trace the evolution of artificial intelligence (AI) in materials science, from large language models as knowledge assistants to autonomous agents that can reason, plan, and use tools. We introduce a two-path framework to analyze this evolution, distinguishing architectural innovation (agent collaboration) from cognitive innovation (learning and representation). This framework synthesizes recent progress in AI-driven discovery, design, and automation. By examining challenges in reliability, interpretability, and physical grounding, we outline a roadmap toward physics-informed, human-AI systems for autonomous scientific discovery.
Ähnliche Arbeiten
UCSF Chimera—A visualization system for exploratory research and analysis
2004 · 47.015 Zit.
AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
2009 · 35.468 Zit.
Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen
1989 · 31.301 Zit.
The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals
2007 · 29.329 Zit.
<i>VESTA 3</i> for three-dimensional visualization of crystal, volumetric and morphology data
2011 · 24.072 Zit.