New Models for Electronic Structure Prediction and Analysis

电子结构预测和分析的新模型

• 批准号: RGPIN-2016-05755
• 负责人: Pearson, Jason
• 金额: $ 1.82万
• 依托单位: University of Prince Edward Island
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=632403
• 关键词: New; Models; Electronic; Structure; Prediction

Most of the worlds major technological and societal challenges are being addressed through research in chemistry, including health, climate change, energy, security, and food supply. With rapid advances in computational power over the last two decades, advanced research computing (ARC) has now become an indispensable research tool in the chemical sciences and many other fields. Research scientists routinely rely on computational simulations to complement, and often exceed, what can be achieved with experiments alone, securing ARC as a transformative modern technology with broad socioeconomic impact in Canada. Unfortunately however, modern computational techniques for modelling chemical systems scale very poorly with molecular size, leading to a practical limit on the scope of molecular species accessible by computational means. Consequently, if computational techniques are to be reliably applied to the ever-increasing scope of important problems in chemistry, radically new computational models are required. Additionally, as the prevalence of computational simulation in chemistry expands, there is a concurrent need for techniques to distill relevant insight and meaning from resultant data, particularly for predictions of detailed electronic structures in quantum chemistry.

世界上大多数重大的技术和社会挑战正在通过化学研究得到解决，包括健康、气候变化、能源、安全和粮食供应。在过去的二十年里，随着计算能力的快速发展，高级研究计算(ARC)现在已经成为化学科学和许多其他领域中不可或缺的研究工具。研究科学家经常依靠计算模拟来补充并往往超过仅靠实验就能实现的成果，从而确保ARC成为一项变革性的现代技术，在加拿大具有广泛的社会经济影响。然而，不幸的是，用于模拟化学体系的现代计算技术与分子大小的比例非常差，导致通过计算手段可获得的分子物种的范围受到实际限制。因此，如果要将计算技术可靠地应用于化学中不断扩大的重要问题的范围，就需要全新的计算模型。此外，随着计算模拟在化学中的普及程度的扩大，同时需要从结果数据中提取相关见解和意义的技术，特别是用于预测量子化学中详细的电子结构。