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REPORT OF THE BASIC RESEARCH NEEDS WORKSHOP for drug development but has hardly been applied to materials science. New methods and algorithms17 are needed for an even more flexible framework that may automatically identify the structure-functionality correlations and the necessary attributes to achieve targeted functionalities. Machine learning will greatly accelerate the rational discovery of materials with multiple functional requirements, such as high energy density, fast charging, long lifetime, and safety. An example of a set of design rules generated by a computer algorithm trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites is shown in Figure 2.4.1.14 The outcome of this model is a set of recommended reaction conditions for the crystallization of inorganic compounds. This example clearly illustrates how discovery of new materials can be systematically implemented based on existing databases of successful and failed experiments. Figure 2.4.1. Graphical representation of the three hypotheses generated from the model, and representative structures for each hypothesis. From Ref. 14. Reproduced with permission of Nature Publishing Group. At the mesoscale level,18 there is a strong need to develop tools for predicting the design and function of mesostructured architectures for energy storage. These architectures include 3D electrode frameworks and interpenetrating phases (dense and porous) that can be assembled into 3D architectures. Genetic algorithms and evolutionary optimization have been applied to MOFs, surveying over a trillion candidate architectures and identifying design modifications with a predicted 400% increase in CO2 absorption capacity over the parent MOF.19 In contrast to MOFs, molecular crystal architectures arise from the balance of many weak interactions, rather than from the strong and predictable bonding patterns of MOFs and COFs. Small changes to the structure of individual molecules can cause profound changes in crystal packing and polymorphism in the crystals they form, making their structures and properties notoriously challenging to predict. A priori design of functional molecular crystals requires a predictive strategy that does not rely on intuitive bonding rules or topologies taken from apparently similar molecules. A new approach exploiting correlations between known single- molecule structures and their functional properties of the crystals they form promises to discover new molecular architectures with targeted functionalities by using only single-molecule structures as input. In a notable success, these energy-structure-function maps discovered new highly porous molecular solids with record low densities.20 These new approaches to architecture discovery offer promising pathways to practical, scalable, safe, and cost-effective smart design of functional mesoscale architectures.21-24 The non-equilibrium charge-discharge cycles of batteries and electrochemical capacitors demand guidance of a higher order than mostly static structural applications. There are volume changes, along with mass transport and dynamic chemo-mechanical interactions, that require special attention and introduce many variables that must be tracked simultaneously. In such complex dynamic systems, in situ experimental interrogation to validate and refine the correlations revealed by machine learning is essential. Examples of such approaches have been recently reported.17 58 PRIORITY RESEARCH DIRECTION – 4PDF Image | Next Generation Electrical Energy Storage
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