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Materials design by evolutionary optimization of functional groups in metal-organic frameworks

机译:通过金属-有机骨架中官能团的进化优化进行材料设计

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摘要

A genetic algorithm that efficiently optimizes a desired physical or functional property in metal-organic frameworks (MOFs) by evolving the functional groups within the pores has been developed. The approach has been used to optimize the CO2 uptake capacity of 141 experimentally characterized MOFs under conditions relevant for postcombustion CO2 capture. A total search space of 1.65 trillion structures was screened, and 1035 derivatives of 23 different parent MOFs were identified as having exceptional CO2 uptakes of >3.0 mmol/g (at 0.15 atm and 298 K). Many well-known MOF platforms were optimized, with some, such as MIL-47, having their CO2 adsorption increase by more than 400%. The structures of the high-performing MOFs are provided as potential targets for synthesis.
机译:已经开发了一种遗传算法,可以通过在孔中演化官能团来有效优化金属有机骨架(MOF)中所需的物理或功能特性。该方法已用于优化与燃烧后二氧化碳捕集相关的条件下141个实验表征的MOF的二氧化碳吸收能力。筛选了总共1.65万亿个结构的搜索空间,并确定了23种不同的母体MOF的1035个衍生物具有> 3.0 mmol / g(在0.15个大气压和298 K下)的异常CO2吸收。对许多著名的MOF平台进行了优化,其中有些平台(例如MIL-47)对CO2的吸附增加了400%以上。提供高性能MOF的结构作为合成的潜在目标。

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