CP-Agent：面向晶体塑性模拟工作流的引擎工程智能体
CP-Agent: A Harness-Engineered Agent for Crystal Plasticity Simulation Workflows
AI总结：
CP-Agent是面向晶体塑性模拟工作流的引擎工程智能体，基于ReAct范式自主执行建模流程，通过工具模式编码领域知识，在四个案例中成功校准参数、复现织构演化，实现自动化与物理可解释性。
AI中文摘要：
晶体塑性（CP）模拟可预测多晶金属的力学行为，但其常规应用受限于配置异构工具、编排多步骤数据流水线以及根据实验校准本构参数所需的手动操作。这些瓶颈阻碍了系统性参数研究的效率，激发了对自动化工作流的兴趣。本研究提出了CP-Agent，一个基于大型语言模型（LLM）的引擎工程智能体，能够从自然语言任务中自主执行完整的CP建模工作流。在ReAct范式下，该智能体推理工具的选择与排序，同时将数值搜索委托给成熟的优化器。该引擎包含极简系统提示、类型化工具定义、调度器以及安全受限的迭代循环，通过工具模式而非硬编码逻辑编码领域知识。CP-Agent在四个案例研究中得到验证：针对增材制造316L不锈钢的拉伸数据校准四个滑移参数；对照已发表的铜基准验证工作流，复现应力-应变和织构演化；恢复铜的初始晶体学织构，智能体正确识别出弥散的初始织构；以及复现Mg-Zn-Ca合金的多道次轧制织构演化，智能体串联五个变形道次并恢复实验观察到的弱化、分裂基面织构。在所有案例中，智能体均从任务陈述中推断出正确的执行顺序，在重复运行中表现稳健，并给出物理上可解释的结果。本工作确立了引擎工程作为自动化CP建模工作流的系统性方法，同时通过可见的推理轨迹保持物理可解释性和可审计性。
英文摘要：
Crystal plasticity (CP) simulations predict the mechanical behavior of polycrystalline metals, yet their routine use is hindered by the manual effort of configuring heterogeneous tools, orchestrating multi-step data pipelines, and calibrating constitutive parameters against experiments. These bottlenecks impede productivity in systematic parameter studies, motivating interest in automated workflows. This study presents CP-Agent, a harness-engineered LLM-based agent that autonomously executes complete CP modeling workflows from natural-language tasks. Operating under the ReAct paradigm, the agent reasons about tool selection and sequencing while delegating numerical search to established optimizers. The harness comprises a minimal system prompt, typed tool definitions, a dispatcher, and a safety-bounded iteration loop, encoding domain knowledge through tool schemas rather than hard-coded logic. CP-Agent is demonstrated on four case studies: calibrating four slip parameters of additively manufactured stainless steel 316L against tensile data; validating the workflow against published copper benchmarks, reproducing stress-strain and texture evolution; recovering the initial crystallographic texture of copper, where the agent correctly identifies a diffuse initial texture; and reproducing the multi-pass rolling texture evolution of a Mg-Zn-Ca alloy, where the agent chains five deformation passes and recovers the experimentally observed weakened, split basal texture. In all cases, the agent inferred the correct execution sequence from the task statement, robustly across repeated runs, and delivered physically interpretable results. This work establishes harness engineering as a systematic approach to automating CP modeling workflows while maintaining physical interpretability and auditability through visible reasoning traces.
