Composite Methodologies Towards Quantitative Predictions across the Periodic Table

跨元素周期表定量预测的综合方法

• 批准号: 1213874
• 负责人: Angela Wilson
• 金额: $ 11.32万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1213874&HistoricalAwards=false
• 关键词: Composite; Methodologies; Towards; Quantitative; Predictions

Angela Wilson of the University of North Texas is supported by an award from the Chemical Theory, Models and Computational Method program in the Chemistry Division to continue developing a novel ab initio composite method - the correlation consistent Composite Approach (ccCA) - that mimics the accuracy achieved by highly correlated methods such as CCSD(T), but at much reduced computational cost. The ccCA methodology is extraordinarily robust, and has been successfully applied not only to ground states, but also to excited states and transition states, to diverse chemistries, from weak interactions to highly energetic species, proving useful for both single reference and multireference wavefunction-based problems and for both metal and nonmetal compounds. The project goal is to develop and apply a family of composite approaches that can be used across the entire periodic table, thereby providing composite methodologies that are applicable to broad classes of problems and chemical systems. Energetic data is often the most critical information needed for rational analysis and design of molecular species and materials. However, one of the long-standing challenges in computational chemistry is in achieving accurate energetics, due to the limited size of molecules that can be studied routinely with high accuracy. The challenges become even more substantial for heavier main group, transition metal, and f-block metal species, where the evolution of practical, high accuracy approaches is much less developed. Ab initio composite strategies have provided a reliable means to predict accurate energetic properties for molecules through the 3d main group. This project addresses these issues by developing strategies for prediction of energetic properties of heavier elements, with application to chemical species and processes of relevance to environmental sustainability. The methods are widely disseminated to the broader scientific community through inclusion in popular computational chemistry software packages. The Wilson group provides scientific training to a broad range of students, from high school to doctoral.

北德克萨斯大学的Angela Wilson获得了化学系化学理论，模型和计算方法项目的奖励，以继续开发一种新的从头合成方法-相关一致的合成方法（ccCA）-模拟高度相关的方法（如CCSD（T））实现的准确性，但计算成本大大降低。ccCA方法非常强大，不仅成功地应用于基态，而且还成功地应用于激发态和过渡态，从弱相互作用到高能物种的各种化学物质，证明对基于单参考和多参考波函数的问题以及金属和非金属化合物都很有用。该项目的目标是开发和应用一系列可用于整个周期表的复合方法，从而提供适用于广泛类别的问题和化学系统的复合方法。 高能数据往往是合理分析和设计分子物种和材料所需的最关键信息。然而，计算化学中长期存在的挑战之一是实现精确的能量学，这是由于可以以高精度进行常规研究的分子的尺寸有限。对于较重的主族、过渡金属和f区金属物质，挑战变得更加巨大，其中实用的高精度方法的发展要少得多。 从头算复合策略提供了一种可靠的手段，通过3d主群预测分子的准确的能量性质。该项目通过制定预测重元素能量特性的战略来解决这些问题，并将其应用于与环境可持续性相关的化学物质和过程。 这些方法通过纳入流行的计算化学软件包而广泛传播到更广泛的科学界。 威尔逊集团为从高中到博士的广泛学生提供科学培训。