In this presentation we will highlight recent advances in machine learning (ML)-driven design of materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification of candidates with desirable properties. More recently, the development of highly accurate ML interatomic potentials and generative models has not only improved the robust prediction of physical properties, but also significantly accelerated the discovery of materials. In the past couple of years, ML methods have enabled high-precision first-principles predictions of electronic and optical properties for large systems, providing unprecedented opportunities in materials science. Concurrently, ML-assisted microstructure reconstruction and physics-informed solutions for partial differential equations have facilitated the understanding of microstructure-property relationships. Most recently, the seamless integration of various ML platforms has led to the emergence of autonomous laboratories that combine quantum mechanical calculations, large language models, and experimental validations, fundamentally transforming the traditional approach to novel materials synthesis. While highlighting recent advances, we also mention existing challenges.
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