Pub Date : 2024-06-12DOI: 10.1088/2632-2153/ad52e9
Yongtao Liu, Marti Checa and Rama K Vasudevan
With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLMs, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed APIs and APIs given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from an inability to extend beyond basic analyses for more in-depth technical experimental design. We argue that an LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows. Such a synergy between human expertise and LLM efficiency in experimentation can open new doors for accelerating scientific research, enabling effective experimental protocols sharing in the scientific community.
随着大型语言模型(LLMs)在开源和专有领域的出现,人们开始关注如何利用这种人工智能(AI)系统来辅助复杂的科学任务,如材料合成、表征、分析和发现。在这里,我们探索了 LLM(尤其是 ChatGPT4)与应用程序接口(API)相结合,在扫描探针显微镜的实验设计、编程工作流和数据分析任务中的实用性,同时使用了内部开发的 API 和商业供应商提供的用于仪器控制的 API。我们发现,LLM 在将实验工作流程的构思转换为显微镜 API 的可执行代码方面特别有用。除了代码生成之外,我们还发现 GPT4 能够对显微图像进行一般意义上的分析。与此同时,我们发现 GPT4 无法超越基本分析,进行更深入的技术实验设计。我们认为,专门针对个别科学领域进行微调的 LLM 有可能成为更好的语言界面,将人类专家的科学想法转换为可执行的工作流程。人类的专业知识与 LLM 在实验中的效率之间的这种协同作用,可以为加速科学研究打开新的大门,使科学界能够共享有效的实验方案。
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Pub Date : 2024-06-11DOI: 10.1088/2632-2153/ad51cd
Dibyakanti Kumar, Anirbit Mukherjee
Physics Informed Neural Networks (PINNs) have been achieving ever newer feats of solving complicated Partial Differential Equations (PDEs) numerically while offering an attractive trade-off between accuracy and speed of inference. A particularly challenging aspect of PDEs is that there exist simple PDEs which can evolve into singular solutions in finite time starting from smooth initial conditions. In recent times some striking experiments have suggested that PINNs might be good at even detecting such finite-time blow-ups. In this work, we embark on a program to investigate this stability of PINNs from a rigorous theoretical viewpoint. Firstly, we derive error bounds for PINNs for Burgers’ PDE, in arbitrary dimensions, under conditions that allow for a finite-time blow-up. Our bounds give a theoretical justification for the functional regularization terms that have been reported to be useful for training PINNs near finite-time blow-up. Then we demonstrate via experiments that our bounds are significantly correlated to the