COMPUTATIONAL MODELING AND DESIGN OF MATERIALS FOR ENERGY CONVERSION AND STORAGE & NEXT GENERATION METHODS

能量转换和存储材料的计算建模和设计

• 批准号: RGPIN-2020-05725
• 负责人: Manzhos, Sergei
• 金额: $ 2.11万
• 依托单位: Institut national de la recherche scientifique
• 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: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717794
• 关键词: COMPUTATIONAL; MODELING; DESIGN; MATERIALS; ENERGY

Design of novel functional materials is the cornerstone in the development of practically all currently pursued technologies aiming to improve life and to help transition to a sustainable society. This includes materials for energy conversion and storage technologies such as solar cells and batteries; of catalysts for fuel cells, environmental cleanup, chemicals synthesis, etc. Computer modeling is a key component of such development as it allows understanding the mechanisms and thereby enables rational rather than ad hoc materials design. It also saves resources which would otherwise be spent on experimentation. Modeling also allows computing properties not directly accessible in an experiment. The key functionality of materials for the above-mentioned technologies critically depends on energies and occupancy of electronic states. Quantum mechanics based modeling is therefore essential. I will implement a research program in atomistic, quantum-mechanics based modeling and design of functional materials for energy conversion and storage, specifically for novel types of metal-ion batteries and solar cells and LED. Energy conversion and storage go together. On one hand, development of novel sustainable, scalable and non-expensive solar cells and LED is a critical part of the future energy mix, on the other, intermittence of clean generation technologies such as solar and wind and transition to electromobility require battery technologies. There is significant overlap in issues, methods and tools in computational modeling of materials for these technologies. These directions therefore will be pursued together in a comprehensive research program. Specific areas of focus are (i) Active electrode materials for post-lithium and organic metal-ion batteries. The combined focus on abundant and cheap active cations such as Na, K, Mg, Al etc and on inexpensive organic or inorganic hosts is extremely promising to realize sustainable and scalable batteries for applications requiring different combinations of performance characteristics. (ii) Charge transport materials for perovskite-based solar cells and LED. These materials are a key bottleneck on the way to commercialization of these technologies. Practical functional materials for novel types of batteries, solar cells, and LED will come out of this program. A critical component of my program is method development to alleviate bottlenecks in commonly used computational methods and tools. I will continue development of the large-scale ab initio method of orbital-free DFT and of the method for accurate computational spectroscopy at interfaces. This is a long-term oriented part of the program which will produce significant know-hows. Machine learning will be used in these projects. The results will allow accurate larger-scale, more realistic yet routine materials modeling. Highly qualified personnel trained on this program will significantly enhance the pool of expertise for Canadian academia and industry.

新型功能材料的设计是目前几乎所有旨在改善生活和帮助过渡到可持续社会的技术发展的基石。这包括用于能量转换和存储技术的材料，如太阳能电池和电池;用于燃料电池的催化剂，环境净化，化学合成等。计算机建模是这种发展的关键组成部分，因为它可以理解机制，从而使合理的，而不是特设的材料设计。它还节省了原本用于实验的资源。建模还允许计算在实验中无法直接访问的属性。 用于上述技术的材料的关键功能关键取决于能量和电子态的占据。因此，基于量子力学的建模至关重要。我将实施一项研究计划，在原子，量子力学为基础的建模和设计功能材料的能量转换和存储，特别是新型金属离子电池和太阳能电池和LED。能源转换和储存是相辅相成的。一方面，开发新型可持续、可扩展和廉价的太阳能电池和LED是未来能源结构的关键部分，另一方面，太阳能和风能等清洁发电技术的发展以及向电动汽车的过渡需要电池技术。在这些技术的材料计算建模中，问题、方法和工具有很大的重叠。因此，这些方向将在一个全面的研究计划中一起进行。 具体的重点领域是（i）后锂电池和有机金属离子电池的活性电极材料。对丰富且廉价的活性阳离子（如Na、K、Mg、Al等）和廉价的有机或无机主体的组合关注对于实现用于需要不同性能特征组合的应用的可持续且可扩展的电池是非常有希望的。(ii)钙钛矿太阳能电池和LED用电荷传输材料。这些材料是这些技术商业化道路上的关键瓶颈。用于新型电池、太阳能电池和LED的实用功能材料将从该计划中产生。 我的计划的一个关键组成部分是方法开发，以减轻常用的计算方法和工具的瓶颈。我将继续发展大规模的从头算方法的轨道自由密度泛函理论和方法的准确计算光谱的接口。这是该计划的长期导向部分，将产生重要的专业知识。机器学习将在这些项目中使用。结果将允许更大规模，更现实，但常规的材料建模。 高素质的人员在这个方案的培训将大大提高专业知识库为加拿大学术界和工业界。