Machine-learning quantum surrogate models to simulate energy transport across interfaces

机器学习量子替代模型来模拟跨界面的能量传输

• 批准号: 2886134
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886134
• 关键词: Machine; learning; quantum; surrogate; models

Modern technologies such as photocatalysis or laser nanolithography involve energy transfer across interfaces. Many critical societal challenges require that we transfer light or electronic energy more efficiently into chemical energy, e.g., to utilize CO2 as renewable fuel. To achieve this, we need to understand the mechanisms behind the intricate dynamics that unfold at interfaces. Quantum mechanical simulations provide electronic-structure insights but are computationally intractable for relevant systems. The aim of this project is to create and apply machine learning models that emulate the quantum mechanical interaction of light, electrons, and atoms for many thousands of atoms at realistic interfaces.

现代技术，如激光或激光纳米光刻，涉及跨界面的能量转移。许多关键的社会挑战要求我们将光能或电子能更有效地转化为化学能，例如，利用二氧化碳作为可再生燃料。为了实现这一点，我们需要了解界面上展现的复杂动态背后的机制。量子力学模拟提供了电子结构的见解，但对于相关系统来说，计算上是棘手的。该项目的目的是创建和应用机器学习模型，模拟光，电子和原子在现实界面上的数千个原子的量子力学相互作用。