C-Photo: Computational photochemistry and in silico design of MOST systems

C-Photo:计算光化学和 MOST 系统的计算机设计

基本信息

项目摘要

The utilization of solar energy is the most promising pathway to cover the energy demands of our modern society. A largely unexplored concept is in this context the molecular solar thermal (MOST) energy conversion process, which releases heat through photochemically triggered chemical transformations on demand. In the proposed research unit FOR MOST, this concept is to be explored from a fundamental research point of view in concert of synthesis, spectroscopy, theory and application. This proposal covers the theoretical part with emphasis on the photochemical and spectroscopical aspects. In detail, genetic algorithms and machine learning approaches will be used to explore the vast chemical space of different MOST systems by pre-screening for the most promising MOST molecules, so-called mostophores. Further detailed quantum chemical investigations using our self-developed excited-state methods but also all other available theoretical tools complement the efforts. The “best” candidates will then be synthesized and spectroscopically investigated with focus on optimal MOST properties, for example, a high energy-to-mass ratio, favorable overlap with the solar spectrum and appropriate storage times together with the other FOR MOST partners. Within this project, we will investigate in detail the photochemical switching mechanisms into the storage state of coupled azobenzenes, azaborines and norbornadiene as well as of hybrid systems. We will thereby help to design novel mostophores by a priori in silico design, by straightforward derivatization as well as by exploitation of molecular interactions for the stabilization of the storage state. A further important contribution of this project is the simulation of optical spectra of the mostophores in their molecular environments to guide the interpretation of static and time-resolved experimental spectra using our self-developed quantum chemical methodology and environment models. Along the same lines, we will investigate the possibility of oxidative and/or reductive switching of the mostophores, i.e. by electron detachment and attachment. The switching mechanisms will be computed and electronic spectra of the intermediates will be simulated to guide experimental investigations. The theoretical efforts undertaken in this project are thus well embedded into the FOR MOST research unit and will contribute substantially the collaborative projects.
利用太阳能是满足现代社会能源需求最有希望的途径。在此背景下,一个很大程度上尚未开发的概念是分子太阳能热(MOST)能量转换过程,该过程通过光化学触发的化学转化释放热量。在提议的研究单位FOR MOST中,这一概念将从合成,光谱学,理论和应用的基础研究角度进行探索。这一建议涵盖了理论部分,重点是光化学和光谱方面。具体来说,遗传算法和机器学习方法将被用于探索不同MOST系统的巨大化学空间,通过预先筛选最有希望的MOST分子,即所谓的MOST分子。进一步详细的量子化学研究使用我们自己开发的激发态方法,以及所有其他可用的理论工具补充的努力。然后将对“最佳”候选材料进行合成和光谱研究,重点关注最佳的MOST特性,例如高能量质量比,与太阳光谱有利的重叠以及与其他for MOST伙伴一起适当的存储时间。在本项目中,我们将详细研究偶氮苯、氮杂氮和降冰片二烯偶氮化合物以及杂化体系的光化学转换机制。因此,我们将通过先验的硅设计,直接衍生化以及利用分子相互作用来稳定存储状态来帮助设计新的大多数载体。本项目的另一个重要贡献是在其分子环境中模拟最孢子的光谱,以指导使用我们自行开发的量子化学方法和环境模型解释静态和时间分辨实验光谱。沿着同样的思路,我们将研究氧化和/或还原性开关的可能性,即通过电子分离和附着。将计算开关机制,并模拟中间体的电子谱以指导实验研究。因此,在这个项目中进行的理论工作很好地融入了FOR MOST研究单元,并将对合作项目做出重大贡献。

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)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Professor Dr. Andreas Dreuw其他文献

Professor Dr. Andreas Dreuw的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Professor Dr. Andreas Dreuw', 18)}}的其他基金

Quantum chemical study of the operating mechanism of the light protection protein dodecin
光保护蛋白dodecin作用机制的量子化学研究
  • 批准号:
    319193282
  • 财政年份:
    2016
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Non-adiabatic dynamics of electronically excited linear polyenes: learning from small and medium-sized for long polyenes and their biological function
电子激发线性多烯的非绝热动力学:从中小型学习长多烯及其生物学功能
  • 批准号:
    239673056
  • 财政年份:
    2013
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Intermolecular Coulombic Decay in Biological Systems and Open-Shell Species
生物系统和开壳物种中的分子间库仑衰变
  • 批准号:
    221566449
  • 财政年份:
    2012
  • 资助金额:
    --
  • 项目类别:
    Research Units
Theoretisches Studium elektronisch angeregter Zustände großer Moleküle
大分子电子激发态的理论研究
  • 批准号:
    97072265
  • 财政年份:
    2008
  • 资助金额:
    --
  • 项目类别:
    Heisenberg Professorships
Entwicklung eines additiven, korrigierenden Potentials für Charge-Transfer-Zustände in zeitabhängiger Dichtefunktionaltheorie
时间相关密度泛函理论中电荷转移态的附加校正势的发展
  • 批准号:
    56439979
  • 财政年份:
    2007
  • 资助金额:
    --
  • 项目类别:
    Priority Programmes
Elektronisch angeregte Zustände großer Moleküle, Untersuchung des ultraschnellen Elektronen- und Energietransfers in biologisch relevanten Systemen
大分子的电子激发态,生物相关系统中超快电子和能量转移的研究
  • 批准号:
    5348608
  • 财政年份:
    2001
  • 资助金额:
    --
  • 项目类别:
    Independent Junior Research Groups
Elementary Steps in Gold Photocatalysis
黄金光催化的基本步骤
  • 批准号:
    404389667
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Priority Programmes
Ab initio Simulation of Time-Resolved X-ray Spectroscopy
时间分辨 X 射线光谱的从头算模拟
  • 批准号:
    493826649
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Photo-induced non-adiabatic dynamics of Carotenoids: a systematic investigation of small model systems towards the biological molecule
类胡萝卜素的光诱导非绝热动力学:对生物分子小模型系统的系统研究
  • 批准号:
    468734905
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants

相似国自然基金

Computational Methods for Analyzing Toponome Data
  • 批准号:
    60601030
  • 批准年份:
    2006
  • 资助金额:
    17.0 万元
  • 项目类别:
    青年科学基金项目

相似海外基金

Conference: Travel Grant for the 28th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2024)
会议:第 28 届计算分子生物学研究国际会议 (RECOMB 2024) 旅费补助
  • 批准号:
    2414575
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Conference: Doctoral Consortium at Student Research Workshop at the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
会议:计算语言学协会 (NAACL) 北美分会年会学生研究研讨会上的博士联盟
  • 批准号:
    2415059
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
REU Site: Computational Methods with applications in Materials Science
REU 网站:计算方法及其在材料科学中的应用
  • 批准号:
    2348712
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
REU Site: Computational Number Theory
REU 网站:计算数论
  • 批准号:
    2349174
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Continuing Grant
Integrated Computational and Mechanistic Investigation on New Reactivity and Selectivity in Emerging Enzymatic Reactions
新兴酶反应中新反应性和选择性的综合计算和机理研究
  • 批准号:
    2400087
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Collaborative Research: CIF: Medium: Snapshot Computational Imaging with Metaoptics
合作研究:CIF:Medium:Metaoptics 快照计算成像
  • 批准号:
    2403122
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Collaborative Research: CyberTraining: Pilot: PowerCyber: Computational Training for Power Engineering Researchers
协作研究:Cyber​​Training:试点:PowerCyber​​:电力工程研究人员的计算培训
  • 批准号:
    2319895
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
MFB: Better Homologous Folding using Computational Linguistics and Deep Learning
MFB:使用计算语言学和深度学习更好的同源折叠
  • 批准号:
    2330737
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
CAREER: Computational Design of Single-Atom Sites in Alloy Hosts as Stable and Efficient Catalysts
职业:合金主体中单原子位点的计算设计作为稳定和高效的催化剂
  • 批准号:
    2340356
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Continuing Grant
Machine Learning for Computational Water Treatment
用于计算水处理的机器学习
  • 批准号:
    EP/X033244/1
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了