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The Future of Material Scientists in an Age of Artificial Intelligence
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The Future of Material Scientists in an Age of Artificial Intelligence
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The Future of Material Scientists in an Age of Artificial Intelligence
The Future of Material Scientists in an Age of Artificial Intelligence
Journal Article

The Future of Material Scientists in an Age of Artificial Intelligence

2024
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Overview
Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI‐models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI‐approaches. To structure the discussion, a conceptual framework of an AI‐ladder is introduced. This AI‐ladder ranges from basic data‐fitting techniques to more advanced functionalities such as semi‐autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping‐stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence. In this perspective, the implications of adopting increasingly advanced AI technologies in materials science are discussed. It is considered how to best utilize AI technologies, identify the necessary new skills, and examine the impact they may have on traditional research methodologies. Central to the discussion is a conceptual AI ladder that spans from elementary data fitting to general artificial intelligence.