Climate change is one of the greatest challenges facing humanity. Machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate, ranging from smart grids, disaster management, and improved resilience, to decarbonizing industry and reimaging the built environment, there are many high-impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. On the other hand, the energy consumption and greenhouse gas emissions impact of the information and communications technology (ICT) sector is significant and dynamic. Estimates of “business as usual” future energy and emissions impacts of ICT projects are upwards of 20% of global energy use and carbon dioxide emissions by 2030. These estimates are without consideration of the ubiquitous use of deep neural networks and well before the advent of the large language model. The complexity in architecture, usage patterns, and size of the parameter space associated with generative AI and large language models comes with staggering increased energy use implications of computing both in training and inference. This panel will describe efforts to leverage these tools while addressing their computational burden, to support mitigation efforts such as decarbonizing electricity systems, enhancing natural ecosystems, and achieving sustainable materials design and manufacture.