Computational modelling of catalytic processes and screening of MOST compounds

在开发基于MOST的储能技术时，详细了解底层的MOST偶对对于优化储能、控制能垒和调节催化能释放至关重要。这个项目将着重于对大多数系统中发生的基态过程的基本理解。因此，我们将开发和筛选MOST系统，并研究表面驱动的催化过程以及分子内和分子间非共价相互作用对MOST性能的影响。为此，我们将使用理论化学、材料建模和机器学习的方法来描述单个分子、相互作用分子、表面和固态结构，以获得对MOST系统的结构、能量和特性相关的见解。为此，我们将进行筛选研究，以优化分子设计并推导结构-性能-关系（工作包1）。我们还将研究MOST对多相催化能量释放的反应机制，以获得对吸附结构和反应机制的基本理解（工作包2）。此外，我们将探索MOST分子之间的相互作用以及MOST分子与纳米粒子壳结构之间的相互作用对MOST关键参数的影响（工作包3）。在这些研究方向上，该项目旨在解决三个关键问题：(i)我们能否通过使用量子化学方法和机器学习工具进行筛选研究，推导出特定MOST对的结构-性质关系，以指导多种分子参数的合成和优化？（ii）在多相催化能量释放和电化学触发反应过程中，界面上的基本反应途径是什么？（iii）如何理解环境对大多数夫妇的影响，并将其用于优化大多数特性？我们的项目将在FOR MOST中发挥重要作用，提供具有优化性能的定制MOST分子的设计策略，对MOST系统与催化剂和表面接触的反应机制的基本理解，以及使用非共价相互作用来定制和优化MOST系统性能的基本概念。

When developing storage technologies based on MOST, it is essential to understand the underlying MOST couples in detail to optimize the storage energy, control the energy barriers and tune the catalytic energy release. This project will focus on the fundamental understanding of the ground state processes that occur for MOST systems. We will therefore develop and screen MOST systems as well as investigate surface-driven catalytic processes and the influence of intra- and intermolecular non-covalent interactions on MOST properties. For this purpose, we will describe individual molecules, interacting molecules, surfaces and solid states structures using the methods of theoretical chemistry, material modelling and machine learning to gain structural, energetic and property-related insights into MOST systems. To this aim, we will perform screening studies to optimise the molecular design and derive structure-property-relationships (work package 1). We will also investigate the reaction mechanisms of heterogeneously catalysed energy release in MOST couples to gain fundamental understanding of adsorption structures and reaction mechanisms (work package 2). In addition, we will explore the influence of interactions between MOST molecules and between MOST molecules and shell-structures of nanoparticles on MOST key parameters (work package 3). With these research directions, the project aims to address three key questions: (i) Can we derive structure-property relationships for specific MOST couples through screening investigations using quantum chemical methods and machine learning tools to guide the synthesis and optimisation of multiple molecular parameters? (ii) What are the fundamental reaction pathways at the interface during heterogeneously catalysed energy release and electrochemically triggered reactions? (iii) How can environmental effects on MOST couples be understood and used to optimise MOST properties? Our project will play an important role in FOR MOST by providing design strategies for tailor-made MOST molecules with optimised properties, a fundamental understanding of the reaction mechanism of MOST systems in contact with catalysts and surfaces and fundamental concepts to use non-covalent interactions to tailor and optimize the performance of MOST systems.