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: 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=709949
• 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. Recent work in our group has made significant strides toward providing innovative solutions to these important problems in chemistry. We have worked to establish a comprehensive, open, and interactive data management platform for the chemical sciences with capabilities for pattern recognition and complex queries. These new technologies for data-driven inquiry will allow researchers to distill new knowledge from a vast chemical data landscape, which promises to yield new industrial catalysts, advanced materials, medicines, and transformative new computational simulation techniques among countless other substantial applications. In particular, we will utilize the technology to design radically new computational models based on machine learning algorithms applied to large datasets. Additionally, we will develop electronic structure analysis tools that predict and interpret the distribution of electrons within chemical bonds and lone pairs, which affords unique qualitative and quantitative insight into how molecular species interact and react.

世界上大多数重大的技术和社会挑战都是通过化学研究来解决的，包括健康、气候变化、能源、安全和食品供应。在过去的二十年里，随着计算能力的快速发展，先进的研究计算（ARC）已经成为化学科学和许多其他领域不可或缺的研究工具。研究科学家通常依靠计算模拟来补充，甚至经常超过单独实验所能取得的成果，从而确保ARC成为一项具有广泛社会经济影响的变革性现代技术。然而，不幸的是，用于模拟化学系统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种类范围的实际限制。因此，如果要将计算技术可靠地应用于化学中日益广泛的重要问题，就需要全新的计算模型。此外，随着计算模拟在化学领域的普及，同时也需要从结果数据中提取相关见解和意义的技术，特别是在量子化学中详细电子结构的预测。