How AI and Automation Can Help in Experimental Materials Research

Innovation in energy storage and conversion is essential for addressing global challenges such as climate change. Artificial intelligence (AI) has emerged as a powerful tool to accelerate materials discovery, but there are still challenges in realizing the potential of computational designs in the laboratory. One question increasingly get asked on self-driving labs is that 'will robots replace scientists?' In this talk, I will discuss, rather than replacing researchers, how emerging technologies can augment and amplify human expertise, leading to unprecedented breakthroughs in energy materials, device and systems. I will present examples of data-driven approaches that can address atomic-to-device level challenges in materials science. I will focus on how to predict experimental outcomes, explain results with interpretable machine learning, and design new experiments that incorporate physical knowledge into an automated framework, thereby guiding the discovery of new materials.