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energy or power density, perhaps coated with inactive membrane-like materials to selectively pass the working ion but block unwanted side reactants. Such a 3D architecture can house anode-only or cathode-only nanoparticles, allowing access of liquid electrolyte to each cell to promote electrochemical reaction in all cells simultaneously. Alternatively, alternating cells may contain anode and cathode nanoparticles to achieve short interaction distances and high power. The architectural framework can contain catalysts to promote the targeted electrochemical reactions, or catalysts can co-occupy cells along with active materials. The design parameters of such cell-based architectures are nearly limitless; examples are just beginning to be explored.8,9 Guided Synthesis of Complex Materials: High-throughput DFT computation of hundreds or thousands of candidate materials has become a standard approach for identifying new materials.10-13 This materials genome approach produces comprehensive libraries of comparative properties and reveals trends that otherwise may go unnoticed by human intuition. Once a material has been selected, the major challenge is synthesis of the targeted material. Synthesis of single-phase compounds typically identified by today’s genomic approaches can require months of trial and error. Synthesis of composite materials comprising several compounds or phases that may be identified by tomorrow’s genomic approaches will be even more challenging. The emerging area of guided synthesis seeks to replace laborious trial and error with predicted synthesis routes for targeted materials through modeling and simulation combined with in situ monitoring during synthesis to interrogate and refine the synthesis protocol. The synthesis challenge is to identify which chemical reactions will produce the targeted compounds without interference by competing side reactions, and to produce not only the targeted bulk material but also its targeted morphology as a film, nanoparticle, foam, or other nanostructured format. The multiplicity of possible chemical reactions leading to the targeted material composition and the many possible competing morphologies make this a monumental challenge for traditional simulation by DFT. Guided synthesis uses machine learning as an alternative approach where correlations between synthesis conditions and synthesis outcomes predict synthesis routes without the need for first principles understanding of the synthesis mechanism.14 Traditional synthesis relies on researcher intuition about reagent properties and composition ratios that govern synthesis outcomes. Guided synthesis relies on machine learning to discover correlations among synthesis conditions and outcomes that may be sufficiently subtle or obscure to have escaped researcher notice. Extensive databases of attempted synthesis protocols for a class of materials are the training set, including failed experiments that are not normally reported in the literature. The guided synthesis approach, just beginning to be explored, has already achieved some notable successes.14-16 Given sufficient development and experience, this approach has disruptive potential to significantly streamline and permanently alter the way we think about synthesis of targeted complex materials. Defected and disordered phases are an outstanding example of materials that resist simulation by conventional techniques, often requiring extremely large supercells and prohibitive computational time to produce credible results. Defected and disordered materials such as alloys, foams, aerogels, glasses, and porous membranes are commonly used in batteries and electrochemical capacitors. There is a strong need to develop simulation tools that can describe these disordered materials and the synthesis routes that govern their properties. Machine learning to reveal correlations among defects, disorder, and properties is an attractive alternative to first principles supercell approaches. Smart Design of Materials and Architectures: High-throughput DFT methods have also shown significant success in battery materials design.10-13 Machine-learning and data-mining algorithms have recently emerged as viable next steps for rational materials design. Machine learning for materials discovery differs from machine learning for synthesis routes in that the material itself, not the synthesis route, is the target. The concept motivating the use of machine learning methods for materials discovery is the use of correlations between structure and functionality to predict materials and design rules for materials and architectures. Machine learning and data mining require access to large data sets of experimental, theoretical, and computational models that implicitly contain the correlations.14 The information contained in the databases needs to be transformed into a set of attributes that can be recognized by a computer algorithm that analyzes the data for correlations between structure and the desired functionalities. The correlations found by machine learning algorithms can be deeper and unbiased by conventional wisdom than those generated by the chemical intuition and background knowledge of human researchers. Machine learning has been extensively used in the pharmaceutical industry NEXT GENERATION ELECTRICAL ENERGY STORAGE PRIORITY RESEARCH DIRECTION – 4 57PDF Image | Next Generation Electrical Energy Storage
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