AI 中文总结
研究钙钛矿氧化物逆设计，引入 DSL 引导策略，将自然语言规则转化为符号谓词，开发 ORCHESTRA 框架。该策略能增强 LLM 智能体推理能力，无需大量特定任务数据集或额外训练，提升材料设计性能。
AI 中文摘要
通过各种数据和人工智能驱动的策略来高效发现高性能材料，其中逆设计（从所需目标属性生成材料）已成为重要范式。大语言模型（LLMs）为逆材料设计提供了补充途径。本文引入特定领域语言（DSL）引导策略，将自然语言设计规则转化为预定义化学 DSL 编码的符号谓词，以提高 LLM 智能体的推理和设计能力。基于此策略开发了多智能体材料设计框架 ORCHESTRA，并应用于双钙钛矿氧化物的逆设计。结果表明符号谓词有助于 LLM 识别不合理规则、验证新规则并在迭代设计周期中改进规则库。与仅依赖自然语言规则的策略相比，DSL 引导框架有提高材料设计性能的潜力，特别是对于具有挑战性的目标属性。这些发现表明数学和统计基础可增强 LLM 智能体在材料科学中的推理能力，且基于 LLM 的逆设计无需大量特定任务数据集或额外模型训练即可有效进行。
英文摘要
Efficient discovery of high-performance materials has been pursued through a variety of data- and AI-driven strategies, among which inverse design, generating materials from desired target properties, has emerged as an important paradigm. Large language models (LLMs) offer a complementary route for inverse materials design because their reasoning and in-context learning capability can be used not only to propose candidates but also to demonstrate interpretable design principles. In this work, we introduce a domain specific language (DSL)-guided strategy to improve the reasoning and design capability of LLM agents by translating natural language design rules into symbolic predicates encoded in a predefined chemistry DSL. These predicates allow the LLM agent to obtain statistical evidence from the accumulated materials data, enabling the agent to evaluate and refine its own reasoning during the design loop. Based on this strategy, we developed a multi-agent materials design framework, called Operational Rule-grounded CHEmical Search Through Reasoning Agents (ORCHESTRA), and applied it to the inverse design of double perovskite oxides under multiple target-property objectives. The results show that symbolic predicates help the LLM identify unsupported rules, validate newly proposed rules and improve the rule store over iterative design cycles. Compared with a strategy relying only on natural language rules, the DSL-guided framework showed the potential to improve materials design performance, particularly for challenging target properties. These findings suggest that mathematical and statistical grounding can enhance the reasoning capability of LLM agents in materials science and that LLM-based inverse design can be performed effectively without large task-specific datasets or additional model training.
