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<p>Artificial intelligence (AI) powered tools are increasingly used in everyday activities and applications. AI-powered search engines effortlessly provide answers to numerous questions—from online shopping recommendations to medical diagnoses. This feedback seems ready for immediate application, which evokes an allure to use AI-powered tools to streamline a researcher’s laborious work. Can generative AI-powered search engines, leveraging advanced algorithms on vast datasets, be an instrument for accelerating in-spot data retrieval in the material sciences? As an example of AI’s applicability in material modelling, the efficiency of generative AI search engines in calibrating a material modelling approach is hereby investigated. As an example, a well-known and popular constitutive model, i.e., the Johnson–Cook plasticity model, has been chosen to estimate the behaviour of the aluminium alloy AA7020-T651 under a dynamic compression loading. The Johnson–Cook model describes the plastic behaviour of ductile materials under severe loading conditions, including large strains, dynamic strain rates, and elevated temperatures. Although the Johnson–Cook model is typically regarded as a relatively simple phenomenological model that can be fitted with a limited number of experimental tests (especially when compared to physically based constitutive formulations), deriving and validating the Johnson–Cook model still requires thorough experimental investigation and a comprehensive literature review. The current investigation is based on the outcomes generated by the three most popular generative AI platforms with freely accessible versions, which are hereby discussed, i.e., ChatGPT (by OpenAI), Gemini (by Google LLC), and Copilot (by Microsoft Corporation). Although generative AI-powered search engines can rapidly analyse literature, extract relevant data, and predict material responses under specified conditions, their output may result in incorrect or biased data due to searches within public sources, open-access research, and not necessarily professional scientific databases. Therefore, to confirm the ability of generative AI tools to deliver results aligned with the most recent evidence-based findings, a current study verifies how specific, efficient, and accurate the AI-generated response can be in application to a task that is objectively evaluable—fitting the Johnson–Cook plasticity model.</p>

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On the Applicability of Generative Artificial Intelligence in Modelling the Dynamic Behaviour of Materials–Potentials and Pitfalls

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • T. Fras,
  • P. Pawlowski

摘要

Artificial intelligence (AI) powered tools are increasingly used in everyday activities and applications. AI-powered search engines effortlessly provide answers to numerous questions—from online shopping recommendations to medical diagnoses. This feedback seems ready for immediate application, which evokes an allure to use AI-powered tools to streamline a researcher’s laborious work. Can generative AI-powered search engines, leveraging advanced algorithms on vast datasets, be an instrument for accelerating in-spot data retrieval in the material sciences? As an example of AI’s applicability in material modelling, the efficiency of generative AI search engines in calibrating a material modelling approach is hereby investigated. As an example, a well-known and popular constitutive model, i.e., the Johnson–Cook plasticity model, has been chosen to estimate the behaviour of the aluminium alloy AA7020-T651 under a dynamic compression loading. The Johnson–Cook model describes the plastic behaviour of ductile materials under severe loading conditions, including large strains, dynamic strain rates, and elevated temperatures. Although the Johnson–Cook model is typically regarded as a relatively simple phenomenological model that can be fitted with a limited number of experimental tests (especially when compared to physically based constitutive formulations), deriving and validating the Johnson–Cook model still requires thorough experimental investigation and a comprehensive literature review. The current investigation is based on the outcomes generated by the three most popular generative AI platforms with freely accessible versions, which are hereby discussed, i.e., ChatGPT (by OpenAI), Gemini (by Google LLC), and Copilot (by Microsoft Corporation). Although generative AI-powered search engines can rapidly analyse literature, extract relevant data, and predict material responses under specified conditions, their output may result in incorrect or biased data due to searches within public sources, open-access research, and not necessarily professional scientific databases. Therefore, to confirm the ability of generative AI tools to deliver results aligned with the most recent evidence-based findings, a current study verifies how specific, efficient, and accurate the AI-generated response can be in application to a task that is objectively evaluable—fitting the Johnson–Cook plasticity model.