This article addresses the growing use of machine learning (ML) in materials science and the need for rigorous standards to ensure high-quality research. It highlights the importance of openness, reproducibility, and proper validation to avoid inflated expectations and unreliable results. The authors introduce a checklist designed to guide the assessment of ML-based studies, emphasizing transparency and data availability. This initiative aims to maintain scientific rigor and adapt to rapid advancements, ensuring the responsible integration of ML in the materials science community.
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