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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=547188
• 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，减少二氧化碳排放的需求迫在眉睫。由于燃煤发电占世界碳排放量的40%，人们对二氧化碳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策略。二氧化碳捕获和储存涉及从燃烧烟气中洗涤二氧化碳并将其永久储存。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型，导致成本过高。在这里，被称为金属有机框架（mof）的先进材料有潜力实现低能量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的二氧化碳，并且很容易释放出来永久储存。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捉摸，无法进行实验。在拟开展的研究项目中，我们将利用分子尺度的计算机建模结合超级计算资源，生成数十万种假设的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挖掘大量数据集，以揭示mof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，并有望加速mof的发展，以满足这些迫切的清洁能源应用。这项研究计划的影响确实是深远的，因为它可能有一天会导致发现用于大规模、经济有效的二氧化碳捕获和储存的先进材料。