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Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient harvesting, storage, conversion, and management of renewable energy. Researchers globally have begun incorporating machine learning (ML) techniques with the aim of accelerating these advances. ML technologies leverage statistical trends in data to build models for prediction of material properties, generation of candidate structures, optimization of processes, among other uses; as a result, they can be incorporated into discovery and development pipelines to accelerate progress. Here we review recent advances in ML-driven energy research, outline current and future challenges, and describe what is required moving forward to best lever ML techniques. To start, we give an overview of key ML concepts. We then introduce a set of key performance indicators to help compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis), and management (smart grids). Finally, we offer an outlook of potential research areas in the energy field that stand to further benefit from the application of ML.

To enable the accelerated discovery of materials with desirable properties, it is critical to develop accurate and efficient search algorithms. Quantum annealers and similar quantum-inspired optimizers have the potential to provide accelerated computation for certain combinatorial optimization challenges. However, they have not been exploited for materials discovery due to absence of compatible optimization mapping methods. Here we show that by combining cluster expansion with a quantum-inspired superposition technique, we can lever quantum annealers in chemical space exploration for the first time. This approach enables us to accelerate the search of materials with desirable properties order 10-50 times faster than genetic algorithms and bayesian optimizations, with a significant improvement in ground state prediction accuracy. Levering this, we search chemical space for discovery of acidic oxygen evolution reaction (OER) catalysts and find a promising previously unexplored chemical family of Ru-Cr-Mn-Sb-O$_2$. The best catalyst in this chemical family show a mass activity 8 times higher than state-of-art RuO$_2$ and maintain performance for 180 hours while operating at 10mA/cm$^2$ in acidic 0.5 M $H_2SO_4$ electrolyte.

Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracle would revolutionize science and technology as we know them. However, as scientists, we would not be satisfied with the oracle itself. We want more. We want to comprehend how the oracle conceived these predictions. This feat, denoted as scientific understanding, has frequently been recognized as the essential aim of science. Now, the ever-growing power of computers and artificial intelligence poses one ultimate question: How can advanced artificial systems contribute to scientific understanding or achieve it autonomously? We are convinced that this is not a mere technical question but lies at the core of science. Therefore, here we set out to answer where we are and where we can go from here. We first seek advice from the philosophy of science to understand scientific understanding. Then we review the current state of the art, both from literature and by collecting dozens of anecdotes from scientists about how they acquired new conceptual understanding with the help of computers. Those combined insights help us to define three dimensions of android-assisted scientific understanding: The android as a I) computational microscope, II) resource of inspiration and the ultimate, not yet existent III) agent of understanding. For each dimension, we explain new avenues to push beyond the status quo and unleash the full power of artificial intelligence's contribution to the central aim of science. We hope our perspective inspires and focuses research towards androids that get new scientific understanding and ultimately bring us closer to true artificial scientists., Comment: 13 pages, 3 figures, comments welcome!

We report on the discovery of a 2-dimensional copper-bismuth nano sheet from \textit{ab initio} calculations, which we call cubine. According to our predictions, single layers of cubine can be isolated from the recently reported high-pressure CuBi bulk material (metastable at ambient conditions) at an energetic cost of merely $\approx 20$~meV/\AA$^2$, comparable to separating single layers of graphene from graphite. Our calculations suggest that cubine has remarkable electronic and electrochemical properties: It is a superconductor with a moderate electron-phonon coupling $\lambda=0.5$, leading to a $T_c$ of $\approx1$~K, and can be readily intercalated with lithium with a high diffusibility, rendering it a promising candidate material as an anode in lithium-ion batteries., Comment: 7 pages, 6 figures