Applications of machine learning in materials science: From a methodological point of view

Discovering new functional materials with certain properties using high-throughout methods is of vital importance for materials science. The advancement of the machine learning method has provided a new, more efficient pathway for this procedure. Traditional trial-and-error methods have been supplanted by in-silico simulations, which facilitate rapid material discovery. Machine learning (ML) has further accelerated this process by uncovering patterns and relationships within complex computational data sets, thus enhancing predictive abilities for material properties and performance. This integration of computational methods and machine learning holds great potential, promising a future where material limitations are overcome, catalyzing technological breakthroughs. Furthermore, ML also inspired advancements in fields like crystallography and metallurgy, and enhancing energy storage materials. Despite challenges in model interpretability, overfitting, and data quality, ML presents an exciting evolution toward a data-driven discipline. Ultimately, ML promises to reduce the time from materials discovery to deployment, turning material limitations into catalysts for innovation.