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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680950
• 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.**

世界上大多数主要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。随着计算能力在过去二十年中的快速发展，高级研究计算（ARC）现已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，甚至经常超过仅通过实验所能实现的目标，从而确保ARC成为一种变革性的现代技术，在加拿大具有广泛的社会经济影响。然而，不幸的是，现代计算技术建模化学系统的规模非常差的分子大小，导致实际限制的范围内的分子种类可通过计算手段。因此，如果要将计算技术可靠地应用于范围不断扩大的化学重要问题，则需要全新的计算模型。 此外，随着计算模拟在化学中的普及，同时需要从所得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的详细电子结构。* 我们小组最近的工作在为化学中的这些重要问题提供创新解决方案方面取得了重大进展。我们致力于为化学科学建立一个全面、开放和交互式的数据管理平台，具有模式识别和复杂查询的能力。这些用于数据驱动查询的新技术将使研究人员能够从庞大的化学数据景观中提取新知识，这有望产生新的工业催化剂，先进材料，药物和变革性的新计算模拟技术以及无数其他实质性应用。特别是，我们将利用该技术设计基于应用于大型数据集的机器学习算法的全新计算模型。此外，我们将开发电子结构分析工具，预测和解释化学键和孤对电子的分布，这为分子物种如何相互作用和反应提供了独特的定性和定量见解。