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      Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

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          Combining theory and experiment in electrocatalysis: Insights into materials design

          Electrocatalysis plays a central role in clean energy conversion, enabling a number of sustainable processes for future technologies. This review discusses design strategies for state-of-the-art heterogeneous electrocatalysts and associated materials for several different electrochemical transformations involving water, hydrogen, and oxygen, using theory as a means to rationalize catalyst performance. By examining the common principles that govern catalysis for different electrochemical reactions, we describe a systematic framework that clarifies trends in catalyzing these reactions, serving as a guide to new catalyst development while highlighting key gaps that need to be addressed. We conclude by extending this framework to emerging clean energy reactions such as hydrogen peroxide production, carbon dioxide reduction, and nitrogen reduction, where the development of improved catalysts could allow for the sustainable production of a broad range of fuels and chemicals.
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            Opportunities and challenges for a sustainable energy future.

            Access to clean, affordable and reliable energy has been a cornerstone of the world's increasing prosperity and economic growth since the beginning of the industrial revolution. Our use of energy in the twenty-first century must also be sustainable. Solar and water-based energy generation, and engineering of microbes to produce biofuels are a few examples of the alternatives. This Perspective puts these opportunities into a larger context by relating them to a number of aspects in the transportation and electricity generation sectors. It also provides a snapshot of the current energy landscape and discusses several research and development opportunities and pathways that could lead to a prosperous, sustainable and secure energy future for the world.
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              Regularization and variable selection via the elastic net

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                Author and article information

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                Journal
                Nature Reviews Materials
                Nat Rev Mater
                Springer Science and Business Media LLC
                2058-8437
                December 2022
                August 24 2022
                : 7
                : 12
                : 991-1009
                Article
                10.1038/s41578-022-00466-5
                23d3ba38-65fa-4a06-82f2-5bb16470282f
                © 2022

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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