Towards chemical space exploration for functional nanostructured systems

走向功能纳米结构系统的化学空间探索

• 批准号: 512350771
• 负责人: Professorin Dr. Carmen Herrmann
• 金额: --
• 依托单位: Institut für Physikalische und Theoretische Chemie
• 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/512350771?language=en
• 关键词: Towards; chemical; space; exploration; functional

Nanostructured and (supra-) molecular materials offer intriguing solutions for applications such as catalysis, biomedicine, thermoelectrics, batteries, and sensing. Our overall objective is to understand and design structural models of functional nanostructured systems, with two representative design goals of ferromagnetic coupling over large distances and narrow conductance distributions in fluctuating molecular junctions. For this purpose, we will develop and implement a reliable and efficient theoretical methodology for evaluating a) exchange spin coupling constants in dinuclear transition metal complexes and b) conductances for fluctuating molecular junctions. For efficiency, we will rely on machine learning, and for reliability, we will employ methods that provide intrinsic error measures. Combined with active learning, this with help us lay the groundwork for exploring the chemical space of functional nanostructured systems. Furthermore, our goal is to gain as much insight as possible from our machine learning models. This will allow us not only to understand structure-property relationships but also robust extrapolation and to understand why a certain machine learning model is successful or not. As a long-term vision, extensions towards large systems such as molecular layers on surfaces or metal-organic frameworks can allow for predictions relevant for technological applications in, e.g., catalysis, sensing, gas separation, and spintronics.

纳米结构和（超）分子材料为催化、生物医学、热电、电池和传感等应用提供了有趣的解决方案。我们的总体目标是理解和设计功能纳米结构系统的结构模型，具有两个代表性的设计目标，即大距离的铁磁耦合和波动分子结中的窄电导分布。为此，我们将开发和实施一个可靠的和有效的理论方法来评估a）交换自旋耦合常数在双核过渡金属配合物和B）波动的分子结的电导。为了提高效率，我们将依靠机器学习，为了提高可靠性，我们将采用提供内在误差度量的方法。结合主动学习，这有助于我们为探索功能纳米结构系统的化学空间奠定基础。此外，我们的目标是从我们的机器学习模型中获得尽可能多的洞察力。这将使我们不仅能够理解结构-性质关系，而且还能进行稳健的外推，并理解为什么某个机器学习模型是成功的。作为一个长期的愿景，向大型系统（如表面上的分子层或金属有机框架）的扩展可以实现与技术应用相关的预测，例如，催化、传感、气体分离和自旋电子学。