arXiv:2512.19458v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly embedded in agentic frameworks for scientific discovery. First-principles materials computation imposes a demanding standard for autonomy: successful execution depends on internally consistent inputs, supervision of long-running calculations, and verified outputs. Here we present VASP Agent, a coding-agent-centered system that combines reusable domain skills, deterministic tools, workspace-state inspection, runtime evidence, and scientific guardrails to execute multi-step VASP calculations. The sy
The increasing sophistication of Large Language Models and agentic frameworks is enabling their application to complex scientific discovery tasks, including first-principles materials computation.
This development indicates a significant step towards autonomous scientific research, potentially accelerating materials discovery and innovation by automating sophisticated computational workflows.
Materials science research can become more automated and efficient, reducing human supervision required for complex simulations and potentially democratizing access to advanced computational methods.
- · Materials scientists
- · Chemical companies
- · Pharmaceutical companies
- · AI software developers
- · Traditional high-throughput screening labs
- · Manual computational researchers
Accelerated discovery of new materials with novel properties.
Reduced R&D cycles and costs for material-dependent industries like battery technology or semiconductors.
Enhanced national competitiveness in advanced materials leading to shifts in industrial leadership.
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Read at arXiv cs.AI