Novel approaches to interacting and disordered systems

交互和无序系统的新方法

• 批准号: 2737041
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2737041
• 关键词: Novel; approaches; interacting; disordered; systems

Disordered quantum systems continue to play a key role in theoretical developments of modern condensed matter physics. Originally, this is due to Anderson's realization in 1958 that the quantum wave nature of condensed matter can lead to interference effects that suppress the classically expected charge transport mechanisms - the birth of Anderson localization. Already in Anderson's paper, it was discussed how the situation might be influenced by many-body effects. In the last decade, following the increase in computational power to allow the construction of meaningfully large Hilbert spaces even for interacting systems, this interplay of disorder and many-body physics has received much attention but remains restricted to small system sizes. What really is needed is a novel approach at solving the underlying Schrödinger equations. Recently, machine learning and deep learning have emerged as numerical techniques that use strategies of artificial intelligence to predict outcomes of numerical experiments. It therefore seems possible to use DL techniques to speed up such sparse matrix codes to construct well-approximated eigenstates for very large system size.

无序量子系统在现代冷凝物理物理学的理论发展中继续发挥关键作用。最初，这是由于安德森（Anderson）在1958年意识到凝结物质的量子波性质会导致干扰效应，从而抑制经典的预期电荷运输机制 - 安德森本地化的诞生。已经在安德森（Anderson）的论文中讨论了这种情况如何受到多体效应的影响。在过去的十年中，随着计算能力的增加，即使在相互作用的系统中，这种疾病和多体物理学的相互作用也引起了很多关注，但仍然仅限于小型系统尺寸。真正需要的是一种新的方法来解决基本的schrödinger方程。最近，机器学习和深度学习已成为使用人工智能策略来预测数值实验结果的数值技术。因此，似乎可以使用DL技术来加快此类稀疏的矩阵代码，以构建非常庞大的系统尺寸的良好特征。