High Throughput Computational Methods to Accelerate Materials Discovery for Clean Energy Applications

气候变化被认为是我们时代的重大挑战之一，迫切需要减少二氧化碳（CO2）排放。由于燃煤发电占世界碳排放量的40%，因此人们对CO2捕获和储存产生了浓厚的兴趣，因为它代表了在短期内减少温室气体排放的实用战略。CO2捕获和储存涉及从燃烧烟道气中洗涤CO2并永久储存它。大规模碳捕获和储存的主要障碍是目前的捕获技术过于能源密集，导致成本过高。在这里，被称为金属有机框架（MOFs）的先进材料有可能实现低能量和低成本的CO2捕获，因为它们可以选择性地吸附大量的CO2并容易地将其释放用于永久储存。然而，为了使基于MOF的技术具有成本效益，需要更高的选择性、吸收能力和稳定性。不幸的是，这些材料的合理设计被搁置，因为这些材料如何捕获气体的分子水平细节仍然难以实验。在拟议的研究计划中，我们将使用分子尺度计算机建模结合超级计算资源来生成数十万个假设的MOF结构，并对其气体吸附能力进行虚拟筛选。然后将使用所谓的化学信息学方法来挖掘庞大的数据集，以揭示MOFs的关键结构和化学特征，从而优化其特定应用的功能特性。确定化学家目标的关键设计特征将使合理的设计成为可能，并有望加速这些紧迫的清洁能源应用的MOFs的开发。 这项研究计划的影响确实可能是深远的，因为它可能有一天会导致发现用于大规模，具有成本效益的二氧化碳捕获和储存的先进材料。

Climate change is considered to be one of the great challenges of our time and the need to mitigate carbon dioxide (CO2) emissions is urgent. Since coal combustion to generate power accounts for 40% of the world's carbon emissions, there is intense interest in CO2 capture and storage because it represents a practical strategy to reduce greenhouse gas emissions in the near term. CO2 capture and storage involves scrubbing CO2 from the combustion flue gas and permanently storing it. The major barrier to large scale carbon capture and storage is that present capture technologies are too energy intensive resulting in prohibitive costs. Here advanced materials called metal organic frameworks (MOFs) have potential to enable low energy and low cost CO2 capture because they can selectively adsorb large amounts of CO2 and easily release it for permanent storage. However, for MOF based technologies to be cost effective, higher selectivities, uptake capacities and stabilities are required. Unfortunately rational design of these materials is stalled because the molecular level detail of how these materials capture gases remains elusive to experiment. In the proposed research program, we will use molecular scale computer modeling combined with supercomputing resources to generate hundreds of thousands of hypothetical MOF structures, and virtually screen them for their gas adsorption abilities. The vast data sets will then be mined using so-called chemoinformatic methods to unravel the key structural and chemical features of MOFs that will optimize their functional properties for specific applications. Determination of the key design features for chemists to target will enable rational design and promises to accelerate the development of MOFs for these urgent clean energy applications. The impact of this research program could indeed be far reaching as it may one day lead to the discovery of advanced materials that are used in large scale, cost-effective CO2 capture and storage.