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.

光催化或激光纳米光刻等现代技术涉及跨界面的能量转移。许多关键的社会挑战要求我们更有效地将光能或电子能转化为化学能，例如利用二氧化碳作为可再生燃料。为了实现这一目标，我们需要了解界面上展开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起来很困难。该项目的目的是创建并应用机器学习模型，模拟现实界面上数千个原子的光、电子和原子的量子力学相互作用。