Beyond the Orbital Picture: New Computational Approaches to Chemical Phenomena
超越轨道图片:化学现象的新计算方法
基本信息
- 批准号:262010-2013
- 负责人:
- 金额:$ 9.03万
- 依托单位:
- 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:
- 关键词:
项目摘要
Chemists solve problems by making molecules and materials with useful properties. Sometimes a chemical problem can be solved by finding an efficient and environmentally friendly way to make a known substance from readily available starting materials. Computational chemistry software lets chemists explore possible synthetic pathways before they embark on the time-consuming quest to make a substance. Sometimes solving a chemical problem requires creating an entirely new substance. It is impossible to systematically test all possible substances, so chemists use qualitative theoretical rules and quantitative computations to navigate through the astronomical number of possibilities until they find a substance with desirable properties. I develop computational methods and theoretical models for predicting chemical reaction pathways, describing chemical phenomena, and designing molecules with desirable properties. At the most fundamental level, I find new computational approaches for describing how electrons bind atoms into molecules. Conventional approaches to this problem are based on the orbital picture. In the orbital picture, electrons are assumed to move quasi-independently. Conventional approaches therefore fail to describe technologically important substances, like unconventional superconductors and spintronic materials, in which the electrons are strongly correlated. My research group is developing alternatives to the orbital picture. First, we are developing alternative computational methods for computing molecular properties; these methods can help chemists decide whether a proposed reaction pathway is plausible. Second, we are developing orbital-free tools for chemical insight. These tools provide intuitive rules that chemists can use to understand and explore new chemical phenomena. In addition, these tools can be used by machine-learning methods, where a computer (instead of a human chemist) gathers data about the features and properties of known molecules, discovers rules for organizing this data, and uses these rules to predict new molecules with desirable properties.
化学家通过制造具有有用性质的分子和材料来解决问题。有时,化学问题可以通过找到一种有效且环保的方法来解决,即从现成的起始材料中制备已知物质。计算化学软件可以让化学家在开始耗时的物质合成之前探索可能的合成途径。有时解决化学问题需要创造一种全新的物质。系统地测试所有可能的物质是不可能的,因此化学家使用定性理论规则和定量计算来导航天文数字的可能性,直到他们找到一种具有理想特性的物质。我开发计算方法和理论模型,用于预测化学反应途径,描述化学现象,并设计具有理想特性的分子。在最基本的层面上,我发现了新的计算方法来描述电子如何将原子结合成分子。解决这个问题的传统方法是基于轨道图像。在轨道图像中,电子被假定为准独立运动。因此,传统的方法无法描述技术上重要的物质,如非传统的超导体和自旋电子材料,其中电子是强相关的。我的研究小组正在开发轨道图的替代方案。首先,我们正在开发用于计算分子性质的替代计算方法;这些方法可以帮助化学家决定所提出的反应途径是否合理。第二,我们正在开发无轨道的化学洞察工具。这些工具提供了直观的规则,化学家可以用来理解和探索新的化学现象。此外,这些工具可以通过机器学习方法使用,其中计算机(而不是人类化学家)收集有关已知分子的特征和属性的数据,发现组织这些数据的规则,并使用这些规则来预测具有所需属性的新分子。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Ayers, Paul其他文献
Conceptual density functional theory: status, prospects, issues
- DOI:
10.1007/s00214-020-2546-7 - 发表时间:
2020-01-31 - 期刊:
- 影响因子:1.7
- 作者:
Geerlings, Paul;Chamorro, Eduardo;Ayers, Paul - 通讯作者:
Ayers, Paul
Should negative electron affinities be used for evaluating the chemical hardness?
- DOI:
10.1039/c0cp01785j - 发表时间:
2011-01-01 - 期刊:
- 影响因子:3.3
- 作者:
Cardenas, Carlos;Ayers, Paul;Geerlings, Paul - 通讯作者:
Geerlings, Paul
Implementing a National Tractor Safety Program: Using "Whole System in a Room" to Mobilize Partners and Implement Solutions
- DOI:
10.1080/1059924x.2016.1142916 - 发表时间:
2016-04-02 - 期刊:
- 影响因子:2.4
- 作者:
Tinc, Pamela J.;Ayers, Paul;Sorensen, Julie - 通讯作者:
Sorensen, Julie
ROPS designs to protect operators during agricultural tractor rollovers
- DOI:
10.1016/j.jterra.2017.05.003 - 发表时间:
2018-02-01 - 期刊:
- 影响因子:2.4
- 作者:
Ayers, Paul;Khorsandi, Farzaneh;Araujo, Guilherme - 通讯作者:
Araujo, Guilherme
Influence of turning radius on wheeled military vehicle induced rut formation
- DOI:
10.1016/j.jterra.2009.02.004 - 发表时间:
2009-04-01 - 期刊:
- 影响因子:2.4
- 作者:
Liu, Kun;Ayers, Paul;Anderson, Alan - 通讯作者:
Anderson, Alan
Ayers, Paul的其他文献
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{{ truncateString('Ayers, Paul', 18)}}的其他基金
Quantitative and Qualitative Tools for Predicting the Products and Mechanisms of Chemical Reactions
预测化学反应产物和机理的定量和定性工具
- 批准号:
RGPIN-2018-06652 - 财政年份:2022
- 资助金额:
$ 9.03万 - 项目类别:
Discovery Grants Program - Individual
Quantitative and Qualitative Tools for Predicting the Products and Mechanisms of Chemical Reactions
预测化学反应产物和机理的定量和定性工具
- 批准号:
RGPIN-2018-06652 - 财政年份:2021
- 资助金额:
$ 9.03万 - 项目类别:
Discovery Grants Program - Individual
Quantitative and Qualitative Tools for Predicting the Products and Mechanisms of Chemical Reactions
预测化学反应产物和机理的定量和定性工具
- 批准号:
RGPIN-2018-06652 - 财政年份:2020
- 资助金额:
$ 9.03万 - 项目类别:
Discovery Grants Program - Individual
Quantitative and Qualitative Tools for Predicting the Products and Mechanisms of Chemical Reactions
预测化学反应产物和机理的定量和定性工具
- 批准号:
RGPIN-2018-06652 - 财政年份:2019
- 资助金额:
$ 9.03万 - 项目类别:
Discovery Grants Program - Individual
Quantitative and Qualitative Tools for Predicting the Products and Mechanisms of Chemical Reactions
预测化学反应产物和机理的定量和定性工具
- 批准号:
RGPIN-2018-06652 - 财政年份:2018
- 资助金额:
$ 9.03万 - 项目类别:
Discovery Grants Program - Individual
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