Buehler Markus J
Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States.
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States.
ACS Eng Au. 2024 Jan 12;4(2):241-277. doi: 10.1021/acsengineeringau.3c00058. eCollection 2024 Apr 17.
Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design, and manufacturing, including their capacity to work effectively with human language, symbols, code, and numerical data. Here, we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. Moreover, when used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem-solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how fine-tuning endows LLMs with a reasonable understanding of subject area knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty recalling correct information and may hallucinate. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies. The graph-based strategy helps us not only to discern how the model understands what concepts are important but also how they are related, which significantly improves generative performance and also naturally allows for injection of new and augmented data sources into generative AI algorithms. We find that the additional feature of relatedness provides advantages over regular retrieval augmentation approaches and not only improves LLM performance but also provides mechanistic insights for exploration of a material design process. Illustrated for a use case of relating distinct areas of knowledge, here, music and proteins, such strategies can also provide an interpretable graph structure with rich information at the node, edge, and subgraph level that provides specific insights into mechanisms and relationships. We discuss other approaches to improve generative qualities, including nonlinear sampling strategies and agent-based modeling that offer enhancements over single-shot generations, whereby LLMs are used to both generate content and assess content against an objective target. Examples provided include complex question answering, code generation, and execution in the context of automated force-field development from actively learned density functional theory (DFT) modeling and data analysis.
Transformer神经网络展现出了令人期待的能力，特别是在材料分析、设计和制造中的应用，包括其有效处理人类语言、符号、代码和数值数据的能力。在此，我们探索将大语言模型（LLMs）用作一种工具，以支持材料的工程分析，应用于检索有关主题领域的关键信息、提出研究假设、发现不同知识领域之间的机理关系，以及编写和执行基于物理基本事实的用于主动知识生成的模拟代码。此外，当用作具有特定特征、能力和指令的人工智能代理集时，大语言模型可以为分析和设计问题的应用提供强大的问题解决策略。我们的实验聚焦于使用基于材料力学领域训练数据开发的微调模型MechGPT。我们首先确认微调如何赋予大语言模型对主题领域知识的合理理解。然而，当在所学内容的上下文之外进行查询时，大语言模型可能难以回忆起正确信息并可能产生幻觉。我们展示了如何使用检索增强的本体知识图谱策略来解决这一问题。基于图谱的策略不仅帮助我们辨别模型如何理解哪些概念是重要的，还能了解它们之间的关系，这显著提高了生成性能，并且自然地允许将新的和增强的数据源注入到生成式人工智能算法中。我们发现相关性这一附加特征比常规检索增强方法具有优势，不仅提高了大语言模型的性能，还为材料设计过程的探索提供了机理见解。在此以关联音乐和蛋白质等不同知识领域的用例进行说明，此类策略还可以提供一个在节点、边和子图层面具有丰富信息的可解释图谱结构，从而对机理和关系提供具体见解。我们讨论了其他提高生成质量的方法，包括非线性采样策略和基于代理的建模，这些方法比单次生成提供了改进，即大语言模型用于生成内容并根据客观目标评估内容。提供的示例包括在从主动学习的密度泛函理论（DFT）建模和数据分析进行自动力场开发的背景下的复杂问答、代码生成和执行。
