The ability to apply advanced machine learning (ML) to process large amounts of heterogenous data can greatly aid in the understanding, discovery and design of new materials for advanced application like clean energy, sustainable semiconductor manufacturing and drug discovery. The heterogenous nature of data encompasses materials structural data, material property data and qualitative descriptions across various types of materials, including solid-state (e.g., semiconductors, batteries) and molecules. This multitude of multi-modal data types necessitates the application of a diverse of advanced ML techniques across different fields. Additionally, computational materials design is only the first step in the materials creation process, which involves a large range of experimental synthesis and analysis techniques creating additional technical challenges and uncertainties. Overall, applying ML to materials discovery provides a notable platform for interdisciplinary ML research with real-world impactful applications. 
In this talk, I will present an overview of Intel Labs’ research efforts focused on applying ML to materials discovery through a closed-loop discovery paradigm spanning automated materials design, automated material synthesis and automated materials characterization. Based on the closed-loop discovery framework, I will illustrate some example research efforts detailing materials property modeling using geometric deep learning, materials discovery using new generative algorithms and materials understanding through natural language processing.