aiqbench analysis
This paper introduces "agentic programs" in computational materials science, where LLM-based agents combine deterministic algorithms with bounded human-like judgment to autonomously construct atomistic models. By leveraging recent advances in agent harnesses, this approach shifts scientific workflows from traditional hybrid human-computer tasks toward complete delegation for specific production environments. The work represents a significant evolution in applying AI agents to complex scientific domains beyond general-purpose reasoning.
Agentic programs: an emerging form of scientific software in computational materials science
arXiv:2609.00795v1 Announce Type: cross Abstract: Computational materials science has traditionally delegated algorithmic tasks to computers while leaving scientific judgments to humans. We argue that recent LLM-based agent harnesses enable an emerging form of scientific software, agentic programs, that combine deterministic algorithms with bounded LLM-based judgment, task-specific verification, episodic maturation, and complete delegation in production. We illustrate this concept with DeMARS, an agentic program for constructing atomistic models from experimentally measured disordered crystal structures.
Abstract:Computational materials science has traditionally delegated algorithmic tasks to computers while leaving scientific judgments to humans. We argue that recent LLM-based agent harnesses enable an emerging form of scientific software, agentic programs, that combine deterministic algorithms with bounded LLM-based judgment, task-specific verification, episodic maturation, and complete delegation in production. We illustrate this concept with DeMARS, an agentic program for constructing atomistic models from experimentally measured disordered crystal structures.
Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
arXiv:2609.00795 [cond-mat.mtrl-sci]
(or arXiv:2609.00795v1 [cond-mat.mtrl-sci] for this version)
https://doi.org/10.48550/arXiv.2609.00795 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 165 words
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