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Abstract The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome. 

Millions of tons of plastics enter the oceans yearly, and they can be fragmented by ultraviolet and mechanical means into nanoplastics. Here, we report the direct observation of nanoplastics in global ocean water leveraging a unique shrinking surface bubble deposition (SSBD) technique. SSBD involves optically heating plasmonic nanoparticles to form a surface bubble and leveraging the Marangoni flow to concentrate suspended nanoplastics onto the surface, allowing direct visualization using electron microscopy. With the plasmonic nanoparticles co-deposited in SSBD, the surface-enhanced Raman spectroscopy effect is enabled for direct chemical identification of trace amounts of nanoplastics. In the water samples from two oceans, we observed nanoplastics made of nylon, polystyrene, and polyethylene terephthalate—all common in daily consumables. The plastic particles have diverse morphologies, such as nanofibers, nanoflakes, and ball-stick nanostructures. These nanoplastics may profoundly affect marine organisms, and our results can provide critical information for appropriately designing their toxicity studies.