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Materials design remains a very complex challenge in science and engineering, owing to the virtually infinite number of variables such as material composition and synthesis parameters. Traditionally guided by expert intuition and iterative experimentation, the process often involves navigating a multidimensional design space with only fragmentary knowledge, which can be compared to exploring a vast territory with a map that covers only a few percent of the terrain. As a result, identifying optimal material candidates frequently requires extensive trial-and-error, consuming significant time and resources before reaching a viable solution. To accelerate this process, materials scientists are increasingly turning to models based on data to guide their research by prioritizing the next materials to study or synthesis parameters to use. In the last decade, machine learning (ML) has gained popularity in materials design thanks to its efficiency and modularity in using a wide range of datasets, whether theoretical or experimental, large or...
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