Large Language Models (LLMs) have garnered considerable interest due to their
impressive natural language capabilities, which in conjunction with various
emergent properties make them versatile tools in workflows ranging from complex
code generation to heuristic finding for combinatorial problems. In this paper
we offer a perspective on their applicability to materials science research,
arguing their ability to handle ambiguous requirements across a range of tasks
and disciplines mean they could be a powerful tool to aid researchers. We
qualitatively examine basic LLM theory, connecting it to relevant properties
and techniques in the literature before providing two case studies that
demonstrate their use in task automation and knowledge extraction at-scale. At
their current stage of development, we argue LLMs should be viewed less as
oracles of novel insight, and more as tireless workers that can accelerate and
unify exploration across domains. It is our hope that this paper can
familiarise material science researchers with the concepts needed to leverage
these tools in their own research.

我们提供一个对大型语言模型在材料科学研究中的适用性的观点，认为它们处理一系列任务和学科中的模糊需求的能力使它们成为帮助研究人员的强大工具。我们定性地研究了基础的大型语言模型理论，并将其与文献中的相关性质和技术联系起来，然后提供了两个案例研究，展示了它们在大规模任务自动化和知识提取中的应用。我们认为，在当前的发展阶段，大型语言模型应该被视为能够加速和统一领域探索的不知疲倦的工具，而不是新见解的神谕。希望本文能让材料科学研究人员了解到运用这些工具的相关概念。

大语言模型时代的材料科学：一个观点

Materials science in the era of large language models: a perspective

The prediction of chemical synthesis pathways plays a pivotal role in
materials science research. Challenges, such as the complexity of synthesis
pathways and the lack of comprehensive datasets, currently hinder our ability
to predict these chemical processes accurately. However, recent advancements in
generative artificial intelligence (GAI), including automated text generation
and question-answering systems, coupled with fine-tuning techniques, have
facilitated the deployment of large-scale AI models tailored to specific
domains. In this study, we harness the power of the LLaMA2-7B model and enhance
it through a learning process that incorporates 13,878 pieces of structured
material knowledge data. This specialized AI model, named MatChat, focuses on
predicting inorganic material synthesis pathways. MatChat exhibits remarkable
proficiency in generating and reasoning with knowledge in materials science.
Although MatChat requires further refinement to meet the diverse material
design needs, this research undeniably highlights its impressive reasoning
capabilities and innovative potential in the field of materials science.
MatChat is now accessible online and open for use, with both the model and its
application framework available as open source. This study establishes a robust
foundation for collaborative innovation in the integration of generative AI in
materials science.

本研究利用 LLaMA2-7B 模型，结合 13,878 件结构化材料知识数据，通过学习过程增强了 MatChat AI 模型，以预测无机材料合成路径。尽管 MatChat 还需要进一步完善以满足不同的材料设计需求，但本研究无疑突出了其出色的推理能力和在材料科学领域的创新潜力，同时以开源方式提供了 MatChat 的模型和应用框架，为将生成式人工智能与材料科学整合的协作创新奠定了坚实的基础。

MatChat：用于材料科学的大型语言模型和应用服务平台

MatChat: A Large Language Model and Application Service Platform for  Materials Science

Scientific publications follow conventionalized rhetorical structures.
Classifying the Argumentative Zone (AZ), e.g., identifying whether a sentence
states a Motivation, a Result or Background information, has been proposed to
improve processing of scholarly documents. In this work, we adapt and extend
this idea to the domain of materials science research. We present and release a
new dataset of 50 manually annotated research articles. The dataset spans seven
sub-topics and is annotated with a materials-science focused multi-label
annotation scheme for AZ. We detail corpus statistics and demonstrate high
inter-annotator agreement. Our computational experiments show that using
domain-specific pre-trained transformer-based text encoders is key to high
classification performance. We also find that AZ categories from existing
datasets in other domains are transferable to varying degrees.

本研究基于科学出版物的常规修辞结构，将辩证区域的分类扩展到材料科学研究领域，并提出并发布了一个新的数据集，该数据集具有材料科学为中心的多标签注释方案，实验结果表明，使用特定领域的预训练基于 transformer 的文本编码器对高分类性能至关重要。