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

高通量计算方法加速清洁能源应用材料的发现

• 批准号: 239067-2012
• 负责人: Woo, Tom
• 金额: $ 6.27万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=594221
• 关键词: Throughput; Computational; Methods; Accelerate; Materials

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.

气候变化被认为是我们这个时代的巨大挑战之一，减少二氧化碳(CO2)排放的必要性迫在眉睫。由于燃煤发电占全球碳排放量的40%，人们对二氧化碳捕获和封存产生了浓厚的兴趣，因为它代表了一种在短期内减少温室气体排放的切实可行的战略。二氧化碳捕获和储存包括从燃烧的废气中清除二氧化碳并将其永久储存。大规模碳捕获和封存的主要障碍是，目前的捕获技术过于耗能，导致成本过高。在这里，被称为金属有机骨架(MOF)的先进材料有可能实现低能源和低成本的二氧化碳捕获，因为它们可以选择性地吸附大量二氧化碳，并很容易将其释放出来永久储存。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停滞不前，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的计算机模拟结合超级计算资源来生成数十万个假设的MOF结构，并对它们的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挖掘海量数据集，以揭示MOF的关键结构和化学特征，这些特征将针对特定应用优化其功能特性。确定化学家要瞄准的关键设计特征将使合理的设计成为可能，并有望加快MOF的开发，以满足这些紧迫的清洁能源应用。这一研究计划的影响可能确实是深远的，因为它可能会在某一天导致发现先进的材料，这些材料可用于大规模、经济高效的二氧化碳捕获和储存。