Hybrid Interfaces in Thermodynamic Equilibrium (HI-TEq)

热力学平衡中的混合界面 (HI-TEq)

• 批准号: 454392740
• 负责人: Dr. Roman Forker
• 金额: --
• 依托单位: Lehrstuhl für Angewandte Physik / Festkörperphysik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/454392740?language=en
• 关键词: Hybrid; Interfaces; Thermodynamic; Equilibrium; HI

Phase diagrams contain crucial information for both fundamental and applied research, revealing under which pressure (p) and temperature (T) conditions specific phases form. However, for organic-inorganic interfaces, experimental phase diagrams (as function of T and p) are de facto non-existent. At the same time, the computation of phase diagrams, which relies on ab-initio thermodynamics, typically suffers from severe approximations, as vibrational and thermal effects are often neglected.The objective of this project is to develop strategies for obtaining p-T-phase diagrams for organic-inorganic interfaces. On the experimental side, this requires developing approaches to grow interfaces close to thermodynamic equilibrium under well-defined conditions. On the theoretical side, the role of thermal and vibrational effects on the relative energies of different polymorphs need to be assessed. We hypothesize that including these effects is indispensable for accurate predictions and devise a way to incorporate them with the aid of machine learning. To extract maximum insight from the phase diagrams, we also provide an in-depth analysis of which interactions stabilize specific phases.The interfaces will be grown near thermodynamic equilibrium in a novel vacuum chamber housing an almost closed cryoshield and using small molecules to facilitate the theoretical evaluation. The structures will be investigated in situ using distortion-corrected low-energy electron diffraction. The computational prediction and evaluation of phase diagrams a specialized structure search algorithm relying on a combination of density functional theory and machine learning in the form of Bayes Linear Regression.p-T-phase diagrams for organic-inorganic interfaces are presently a blind spot on the map of surface science that urgently needs to be filled. So far, this endeavor could not be undertaken, due to the lack of adequate experimental equipment and due to the computational difficulty to determine systems that are likely to show phase transitions in the experimentally accessible region. Only with the advent of modern DFT methods and machine-learning techniques, such systems can now be tackled.The project will be executed by a collaboration between the FSU Jena, Germany and the TU Graz, Austria. The experiments will be performed in the group of Torsten Fritz and Roman Forker, who are experts for growing organic-inorganic interfaces and the determination of their structures. The theoretical studies will be performed by Oliver T. Hofmann, who is an expert in machine learning and DFT band structure calculations and who will coordinate the project, and by Egbert Zojer, who has extensive experience in modelling organic semiconductors and their interfaces.

相图包含基础研究和应用研究的重要信息，揭示了在压力 (p) 和温度 (T) 条件下形成特定相的情况。然而，对于有机-无机界面，实验相图（作为 T 和 p 的函数）实际上是不存在的。与此同时，依赖从头热力学的相图计算通常会遭受严重的近似，因为振动和热效应经常被忽略。该项目的目标是开发获得有机-无机界面的 p-T 相图的策略。在实验方面，这需要开发在明确的条件下生长接近热力学平衡的界面的方法。在理论方面，需要评估热和振动效应对不同多晶型物相对能量的作用。我们假设，包含这些影响对于准确预测是必不可少的，并设计了一种在机器学习的帮助下将它们结合起来的方法。为了从相图中获得最大的洞察力，我们还深入分析了哪些相互作用稳定了特定的相。界面将在一个新型真空室中接近热力学平衡的位置生长，该真空室装有几乎封闭的冷冻屏，并使用小分子来促进理论评估。将使用畸变校正低能电子衍射对这些结构进行原位研究。相图的计算预测和评估是一种专门的结构搜索算法，依赖于贝叶斯线性回归形式的密度泛函理论和机器学习的结合。有机-无机界面的p-T相图目前是表面科学地图上急需填补的盲点。到目前为止，由于缺乏足够的实验设备，并且由于确定可能在实验可访问区域中显示相变的系统的计算困难，这一努力无法进行。只有随着现代 DFT 方法和机器学习技术的出现，此类系统现在才能得到解决。该项目将由德国耶拿 FSU 和奥地利格拉茨工业大学合作执行。这些实验将由 Torsten Fritz 和 Roman Forker 团队进行，他们是有机-无机界面生长及其结构测定方面的专家。理论研究将由机器学习和 DFT 能带结构计算方面的专家 Oliver T. Hofmann 和 Egbert Zojer 进行，前者是机器学习和 DFT 能带结构计算方面的专家，负责协调该项目，而后者则由 Egbert Zojer 进行，后者在有机半导体及其界面建模方面拥有丰富的经验。