Computational high throughput screening methods and data driven materials design
计算高通量筛选方法和数据驱动的材料设计
基本信息
- 批准号:RGPIN-2019-06867
- 负责人:
- 金额:$ 5.76万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2020
- 资助国家:加拿大
- 起止时间:2020-01-01 至 2021-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
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,将全球变暖限制在1.5ºC将需要到2030年将二氧化碳排放量从2010年的水平下降45%。在如此短的时间内,“碳捕获是人类唯一的希望”,正如前英国政府首席科学家大卫金爵士戏剧性地说的那样。由于世界上约35%的人为CO2排放来自燃烧化石燃料的发电厂,因此对从这种点源的燃烧烟道气中捕获CO2的技术一直很感兴趣。碳捕获和储存(CCS)涉及从燃烧烟气中洗涤CO2并以相对纯的形式永久储存CO2。虽然存在几个大规模的CCS项目,每年捕获和储存超过一百万吨的CO2,但目前在这些项目中使用的基于溶剂的CO2洗涤技术对于大规模部署来说能量成本太高。低能耗CCS替代技术的最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CO2。这应该提供低能量和低成本的CO2捕获,只要可以找到合适的材料,并且迄今为止尚未确定此类材料。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。我们有两条不同的途径来实现这一目标。第一个是将工业过程模拟集成到原子级材料的合理设计中。也就是说,弥合材料设计和工艺工程之间的差距。第二条途径是开发用于材料发现的尖端高通量筛选、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内评估数千至数百万种材料,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。 该研究计划的学员将接受多学科培训,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。虽然该研究计划的重点是开发与绿色房屋气体缓解相关的材料,但预计所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。
项目成果
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{{ truncateString('Woo, Tom', 18)}}的其他基金
Computational high throughput screening methods and data driven materials design
计算高通量筛选方法和数据驱动的材料设计
- 批准号:
RGPIN-2019-06867 - 财政年份:2022
- 资助金额:
$ 5.76万 - 项目类别:
Discovery Grants Program - Individual
Computational high throughput screening methods and data driven materials design
计算高通量筛选方法和数据驱动的材料设计
- 批准号:
RGPIN-2019-06867 - 财政年份:2021
- 资助金额:
$ 5.76万 - 项目类别:
Discovery Grants Program - Individual
Computational high throughput screening methods and data driven materials design
计算高通量筛选方法和数据驱动的材料设计
- 批准号:
RGPIN-2019-06867 - 财政年份:2019
- 资助金额:
$ 5.76万 - 项目类别:
Discovery Grants Program - Individual
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 - 财政年份:2017
- 资助金额:
$ 5.76万 - 项目类别:
Discovery Grants Program - Individual
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 - 财政年份:2016
- 资助金额:
$ 5.76万 - 项目类别:
Discovery Grants Program - Individual
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 - 财政年份:2015
- 资助金额:
$ 5.76万 - 项目类别:
Discovery Grants Program - Individual
Catalyst Modeling and Computational Chemistry
催化剂建模和计算化学
- 批准号:
1219068-2009 - 财政年份:2015
- 资助金额:
$ 5.76万 - 项目类别:
Canada Research Chairs
Catalyst Modeling and Computational Chemistry
催化剂建模和计算化学
- 批准号:
1000219068-2009 - 财政年份:2014
- 资助金额:
$ 5.76万 - 项目类别:
Canada Research Chairs
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 - 财政年份:2014
- 资助金额:
$ 5.76万 - 项目类别:
Discovery Grants Program - Individual
Meeting with Inventys Thermal Technologies Inc in Burnaby BC to discuss potential research partnerships
在不列颠哥伦比亚省伯纳比与 Inventys Thermal Technologies Inc 会面,讨论潜在的研究合作伙伴关系
- 批准号:
451668-2013 - 财政年份:2013
- 资助金额:
$ 5.76万 - 项目类别:
Interaction Grants Program
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Computational high throughput screening methods and data driven materials design
计算高通量筛选方法和数据驱动的材料设计
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