Chemical research often contains inaccurate mass measurement data, according to AI analysis

AI-powered data analysis tools have the potential to significantly improve the quality of scientific publications. A new study by Professor Mathias Christmann, a chemistry professor at Freie Universität Berlin, has uncovered shortcomings in chemical publications.

Using a Python script developed with the help of modern AI language models, Christmann analyzed more than 3,000 scientific papers published in Organic Letters over the past two years. The analysis revealed that only 40% of the chemical research papers contained error-free mass measurements. The AI-based data analysis tool used for this purpose could be created without any prior programming knowledge.

“The results demonstrate how powerful AI-powered tools can be in everyday research. They not only make complex analyses accessible but also improve the reliability of scientific data,” explains Christmann.

Advanced large language models such as ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic) now allow natural language to be translated directly into computer languages like Python. This enables researchers without coding backgrounds to create applications that, for instance, search through large datasets for specific text components or measurement values. The data obtained in this way can then be automatically processed further and checked for plausibility.

Christmann’s study, “What I Learned from Analyzing Accurate Mass Data of 3000 Supporting Information Files,” published in Organic Letters, used an AI-powered data analysis tool to uncover previously unknown systematic errors. It also identified instances where miscalculated values appeared to be validated by measurements.

“These observations raise the question of whether some measurements may have been fabricated,” the researcher emphasizes.

This study demonstrates how AI tools can enhance scientific integrity through automated quality control and systematic error detection.

As part of an “AI in Education” initiative, Freie Universität Berlin’s Department of Biology, Chemistry, Pharmacy plans to integrate these and similar tools into its curriculum. “They will help students develop strong data analysis skills and critical thinking abilities,” says Christmann. “AI tools will be valuable in preparing students for their research careers.”