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Artificial intelligence in action: building simulation and analysis tools for powder diffraction

Year: 2025

Type: article

Abstract: This paper explores the application of generative pre-trained transformer (GPT)-based large language models (LLMs) in the development of simulation and analysis tools for X-ray powder diffraction. We demonstrate how these models enable users with minimal programming experience to generate functional and efficient code through natural language prompts. The discussion highlights both the capabilities and limitations of LLM-assisted coding, offering insights into the practical integration of artificial intelligence for simulating and analysing simple X-ray powder diffraction patterns.

Source: Acta Crystallographica Section A Foundations and Advances

DOI: 10.1107/s2053273325007508

Authors: Paolo Scardi, Marcelo A. Malagutti

Institution: University of Trento

Language: English

Topic: X-ray Diffraction in Crystallography

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,430

Funder: Ministero dell'Università e della Ricerca