利用大型语言模型内部知识进行功能性金属配合物的生成式设计
Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge of Large Language Models
AI总结：
本文提出LLM-EO进化优化框架，利用大型语言模型的内部化学知识生成并优化过渡金属配合物，在单目标与多目标任务中以少量候选取得优于传统遗传算法的效果。
摘要：
设计功能性过渡金属配合物（TMCs）面临挑战，原因在于金属与配体的搜索空间巨大，需要高效的优化策略。传统遗传算法（GAs）被广泛使用，其通过由显式数学目标驱动的随机突变和交叉来探索该空间。然而，在不同GA任务之间迁移知识十分困难。我们将大型语言模型（LLMs）整合到进化优化框架（LLM-EO）中，并将其应用于TMCs的单目标和多目标优化。我们发现，LLM-EO利用LLMs在大规模预训练期间获得的化学知识，超越了传统GAs。值得注意的是，在没有监督微调的情况下，LLMs利用优化过程中的全部历史数据，表现优于那些仅关注最佳性能TMCs的方法。LLM-EO在137万个TMCs空间中仅提出200个候选物，便成功识别出HOMO-LUMO能隙最大的前20个TMCs中的8个。通过使用自然语言进行提示工程，LLM-EO为多目标优化引入了无与伦比的灵活性，从而规避了复杂数学公式的必要性。作为生成模型，LLMs能够通过融合内部知识与外部化学数据，提出具有独特性质的新配体和TMCs，从而结合高效优化与分子生成的优势。随着LLMs作为预训练基础模型的潜力不断增长以及新的训练后推理策略的出现，我们预见基于LLM的进化优化将在化学和材料设计中获得广泛应用。
英文摘要：
Designing functional transition metal complexes (TMCs) faces challenges due to the vast search space of metals and ligands, requiring efficient optimization strategies. Traditional genetic algorithms (GAs) are commonly used, employing random mutations and crossovers driven by explicit mathematical objectives to explore this space. Transferring knowledge between different GA tasks, however, is difficult. We integrate large language models (LLMs) into the evolutionary optimization framework (LLM-EO) and apply it in both single- and multi-objective optimization for TMCs. We find that LLM-EO surpasses traditional GAs by leveraging the chemical knowledge of LLMs gained during their extensive pretraining. Remarkably, without supervised fine-tuning, LLMs utilize the full historical data from optimization processes, outperforming those focusing only on top-performing TMCs. LLM-EO successfully identifies eight of the top-20 TMCs with the largest HOMO-LUMO gaps by proposing only 200 candidates out of a 1.37 million TMCs space. Through prompt engineering using natural language, LLM-EO introduces unparalleled flexibility into multi-objective optimizations, thereby circumventing the necessity for intricate mathematical formulations. As generative models, LLMs can suggest new ligands and TMCs with unique properties by merging both internal knowledge and external chemistry data, thus combining the benefits of efficient optimization and molecular generation. With increasing potential of LLMs as pretrained foundational models and new post-training inference strategies, we foresee broad applications of LLM-based evolutionary optimization in chemistry and materials design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
