Machine Learning Surrogates for Simulating Quantum Materials

用于模拟量子材料的机器学习替代品

• 批准号: 580909-2022
• 负责人: Ortner, ChristophC
• 金额: $ 1.82万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758450
• 关键词: Machine; Learning; Surrogates; Simulating; Quantum

Quantum materials have unusual magnetic and electrical properties that, if understood and controlled, will revolutionize the technology sector, e.g. enable highly energy-efficient electrical systems or faster electronic devices.This project contributes to the development of computationally efficient and robust "virtual laboratories" in which quantum materials systems can be studied far more cheaply and in a more targeted way than in experiments. This will enable high through-put screening of candidate quantum systems and thus accelerate scientific discovery and technology transfer. The barrier to this goal is that, despite the continuing rapid increase in computational resources available to researchers, high-fidelity simulation of quantum materials systems of scientific and technological interest remains out of reach. The critical factors are the scale of such quantum systems and the need to accurately treat strong correlation effects to describe the emergence of genuine quantum phenomena. The objective of this Alliance-Quantum-Catalyst project is to significantly expand the range of quantum systems that can be simulated reliably and accurately by merging modern machine-learning methodology with deep insights into the chemistry and physics of quantum materials systems.

量子材料具有不寻常的磁和电特性，如果理解和控制这些特性，将彻底改变技术领域，例如实现高能效电气系统或更快的电子设备。该项目有助于开发计算效率高且强大的“虚拟实验室”，在虚拟实验室中，可以比实验更便宜、更有针对性地研究量子材料系统。这将使候选量子系统的高通量筛选成为可能，从而加速科学发现和技术转让。这一目标的障碍在于，尽管研究人员可用的计算资源持续快速增加，但对科学和技术感兴趣的量子材料系统的高保真模拟仍然遥不可及。关键因素是此类量子系统的规模以及准确处理强相关效应以描述真正量子现象的出现的需要。该联盟量子催化剂项目的目标是通过将现代机器学习方法与对量子材料系统的化学和物理的深入了解相结合，显着扩大可以可靠、准确地模拟的量子系统的范围。