![]() ![]() Finally, future research efforts should focus on in-depth understandings of descriptors, materials’ ML methods, data-driven application strategies, and integration of studies. A Markov chain algorithm basically determines the next most probable suffix word for a given prefix. This task is about coding a Text Generator using Markov Chain algorithm. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page. We then discuss how AI can assist in each step through the whole life cycle of material discovery (including characterization, property prediction, synthesis, and theory paradigm discovery) by conducting a thorough literature review in the material application section. Markov chain text generator is a draft programming task. This paper answers these questions by first introducing ML methods from a material study perspective in a tutorial section. With the rapid development of AI methods and the complex nature of interdisciplinary research, a challenge is posed as for which methods to choose for different material systems or context and which steps of the material discovery process would stand to benefit. Advances in artificial intelligence (AI), especially machine learning (ML), provide enormous tools for processing complex data generated from experimental and computational materials research.
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