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Solving Advanced Task-Specific Problems in Measurement Sciences with Generative AI.

M Farooq Wahab1, Troy T Handlovic1, Souvik Roy2

  • 1Department of Chemistry & Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States.

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ChatGPT-4 can generate scientific code for complex tasks in chemistry and imaging. Validation and feedback are crucial for accurate results, promoting open science.

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Area of Science:

  • Artificial Intelligence in Scientific Research
  • Computational Chemistry and Spectroscopy
  • Advanced Data Processing in Measurement Science

Background:

  • Generative Pre-trained Transformer (GPT) models like ChatGPT-4 demonstrate capabilities in generating coherent text.
  • The potential of AI in material sciences, synthetic chemistry, and drug discovery is gaining traction.
  • Task-specific AI implementation requires careful prompting and persona assignment.

Purpose of the Study:

  • To evaluate ChatGPT-4's ability to solve advanced, task-specific problems in analytical chemistry, spectroscopy, bioimage super-resolution, and electrochemistry.
  • To assess the level of human direction required for AI to generate scientific code.
  • To explore the potential of AI in advancing open and transparent scientific practices.

Main Methods:

  • Four advanced scientific problems from recent literature were posed to ChatGPT-4.
  • AI was assigned specific personas and targeted questions were formulated.
  • MATLAB code generation was assessed with and without user-provided equations and directions.
  • Validation tests and feedback correction mechanisms were implemented.

Main Results:

  • ChatGPT-4 successfully generated MATLAB code for all four complex scientific tasks.
  • Two tasks (microwave spectrum construction, bioimage super-resolution) were completed with minimal human guidance.
  • Two tasks (baseline correction, diffusion current estimation) required additional user input, including equations.
  • Validation and feedback loops were essential for error detection and correction.

Conclusions:

  • ChatGPT-4 shows significant potential for assisting in advanced scientific problem-solving and code generation.
  • The effectiveness of AI is dependent on the specificity of prompts and the inclusion of necessary scientific context.
  • Implementing validation and feedback mechanisms is critical for reliable AI-driven scientific discovery.
  • AI-assisted code generation can contribute to greater transparency and accessibility in measurement science.