Computational high throughput screening methods and data driven materials design

气候变化是我们这一代人面临的最大挑战之一，减少二氧化碳排放的需求迫在眉睫。在2018年10月的政府间气候变化专门委员会（IPCC）报告中指出，到2030年，将全球变暖限制在1.5ºC将需要二氧化碳排放量在2010年的基础上惊人地下降45%。在如此短的时间内，正如前英国政府首席科学家戴维•金爵士（Sir David King）戏剧性地指出的那样，“碳捕获是人类唯一的希望”。由于世界上约35%的人为二氧化碳排放来自燃烧化石燃料的发电厂，因此人们对从这些点源的燃烧烟气中捕获二氧化碳的技术非常感兴趣。碳捕获和储存（CCS）涉及从燃烧烟气中洗涤二氧化碳并以相对纯净的形式永久储存二氧化碳。虽然有几个大型CCS项目每年捕获和储存超过100万吨的二氧化碳，但目前在这些项目中使用的溶剂型二氧化碳洗涤技术在能源成本上过于昂贵，无法大规模部署。低能耗CCS替代技术的前沿是使用固体吸附剂（而不是液体）在所谓的变压吸附（PSA）系统中捕获二氧化碳。只要能找到合适的材料，这应该能提供低能耗和低成本的二氧化碳捕获，但迄今为止，这种材料尚未被确定。拟议研究的短期和长期目标是开发新的计算工具，以加速新材料的发现，特别关注能够实现低成本CCS的材料。我们有两条截然不同的途径来实现这一目标。首先是将工业过程模拟整合到原子级材料的合理设计中。也就是说，要弥合材料设计和工艺工程之间的差距。第二条途径是为材料发现开发尖端的高通量筛选、数据挖掘和机器学习方法。在这些所谓的数据驱动方法中，在计算机的虚拟空间中评估数千到数百万种材料，以寻找隐藏的模式，这些模式可用于识别潜在的高性能材料以进行合成。该研究项目的学员将接受多学科的培训，这将为他们提供备受追捧的技能，特别是在数据科学和机器学习方面。虽然研究项目的重点是开发与温室气体减排相关的材料，但预计提出的新方法将更广泛地与其他类别的材料和其他应用相关。

Climate change is one of the greatest challenges of our generation and the need to mitigate CO2 emissions is urgent. In the October 2018 Intergovernmental Panel on Climate Change (IPCC) report it is stated that limiting global warming to 1.5ºC would require CO2 emissions to fall by an astonishing 45% from 2010 levels by 2030. With such short timelines “Carbon Capture is the only hope for mankind” as dramatically put by Sir David King, the former UK government chief scientist. Since ~35% of the world's anthropogenic CO2 emissions arise from electrical power generating plants that burn fossil fuels, there has been substantial interest in technology to capture CO2 from the combustion flue gases of such point sources. Carbon capture and storage (CCS) involves scrubbing CO2 from the combustion flue gas and permanently storing the CO2 in relatively pure form. Although several large scale CCS projects exist that capture and store more than a million tons of CO2 per year, the solvent-based CO2 scrubbing technologies currently used in these projects are too energetically costly for wide-scale deployment. At the forefront of alternative technologies for low energy CCS is the use of solid sorbents (instead of liquids) to capture the CO2 within what are called pressure swing adsorption (PSA) systems. This should offer low energy and low cost CO2 capture as long as the right materials can be found and to date such materials have not been identified. The short and long term goals of the proposed research are to develop new computational tools to accelerate the discovery of new materials, with a particular focus on materials that will enable low cost CCS. We have two distinct avenues towards this goal. The first is to integrate industrial process simulations into the rational design of materials at the atomistic level. That is, to bridge the gap between materials design and process engineering. The second avenue is to develop cutting edge high-throughput screening, data-mining and machine learning methods for materials discovery. In these so-called data driven methods, thousands to millions of materials are evaluated within the virtual space of the computer to look for hidden' patterns that can be used to identify potential high performing materials to target for synthesis. Trainees in this research program will receive multi-disciplinary training that will provide them with highly sought after skills, particularly in the data sciences and machine learning. Although the research program focuses developing materials related to green house gas mitigation, the novel methods proposed are expected to be more broadly relevant to other classes of materials and other applications.