Application of machine learning to condensed matter physics

机器学习在凝聚态物理中的应用

• 批准号: 2904795
• 金额: --
• 依托单位: University of Manchester
• 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-2904795
• 关键词: Application; machine; learning; condensed; matter

Machine learning has already proven to be one of the most influential discoveries of our time. It has found widespread applications in almost every scientific field, becoming an essential predictor of important properties of condensed matter materials. Developing purpose-built machine learning algorithms for studying these properties would streamline research in the search for new desirable materials from the extremely vast number of possible constructions. The proposed research will pioneer an interdisciplinary approach, utilising both novel machine learning techniques and quantum computing algorithms to study condensed matter systems. This combination will be well-suited to yield insights into the quantum mechanical behavior of these materials.Condensed matter physics is the study and modeling of solid-state materials using quantum mechanical descriptions. Due to many quantum interactions, finding exact solutions to these descriptions is not feasible. There have been attempts to develop computational methods that can closely approximate these emergent properties so that new materials can be designed with desirable features. Neural networks appear to be an ideal method to analyze these systems. They are highly effective at efficiently searching through large search spaces, making them applicable to condensed matter problems, which feature a broad array of possible material constructions.Purpose-built condensed matter neural networks could provide significant insights into the physics of condensed matter systems. The proposed research aims to redesign neural network algorithms with the guiding principle of removing implicit assumptions already identified in the current formulations. This will increase the symmetry properties of networks, consequently making it particularly applicable to condensed matter systems that feature many analogous symmetries. These redesigns appear to be widely applicable, even outside the domain of condensed matter applications; the resulting research should be influential, with many functions that can be overhauled using this symmetry principle.This project will consist of a novel and large overhaul of many aspects of current neural networks, with far-reaching improvements across many of their applications. Particularly, the approach of removing implicit assumptions and consequently introducing symmetries will have a substantial impact in condensed matter physics and quantum machine learning, with improvements to quantum machine learning aiding further advances in condensed matter modeling.Below is a summary of the expected outcomes of this work:New highly efficient neural network functions exhibiting symmetries applicable to condensed matter systems. These should allow the network to represent and manipulate data more effectively.Improved predictions of the properties of condensed matter systems.Potential to design materials by specifying the desired characteristics.Insight into the nature of how neural networks embed and manipulate data.Interfaces with quantum machine learning and new activation functions that better convey quantum mechanical probabilities.Further advances in recurrent neural network models, with improved memory efficiency. This should allow for an iterative approach to modeling condensed matter systems.

机器学习已经被证明是我们时代最有影响力的发现之一。它几乎在每个科学领域都发现了广泛的应用，成为凝结物质材料重要特性的重要预测指标。开发用于研究这些属性的专用机器学习算法将简化从极限数量可能的结构中寻找新的理想材料的研究。拟议的研究将开拓跨学科的方法，利用新型的机器学习技术和量子计算算法来研究冷凝物质系统。这种组合将非常适合对这些材料的量子机械行为的见解。物理学的物理是使用量子机械描述对固态材料进行的研究和建模。由于许多量子相互作用，找到这些描述的精确解决方案是不可行的。已经尝试开发可以密切近似这些新兴属性的计算方法，以便可以使用理想的功能设计新材料。神经网络似乎是分析这些系统的理想方法。它们在有效地通过大型搜索空间进行有效搜索非常有效，使其适用于凝结物问题，这些问题具有一系列可能的材料构造。Purpose-Purpose构建的凝结物质神经网络可以为凝结物质系统的物理学提供重要的见解。拟议的研究旨在重新设计神经网络算法，其指导原则是删除当前配方中已经确定的隐式假设。这将增加网络的对称性，因此使其特别适用于具有许多类似对称性的凝结物质系统。这些重新设计似乎是广泛适用的，即使在凝结物质应用的领域之外也是如此。由此产生的研究应该具有影响力，可以使用这种对称原则进行大修的许多功能。本项目将包括对当前神经网络的许多方面的新颖而大规模的大修，其许多应用程序都有很大的改善。尤其是，删除隐性假设并因此引入对称性的方法将对凝聚的物理和量子机器学习产生重大影响，并改进了量子机器学习的改进，有助于进一步的凝结物质建模进一步进步。Below是这项工作的预期兴起的汇总：这项工作的预期疲软：高效的神经网络功能高效地表现出浓缩功能的系统。这些应该允许网络更有效地表示和操纵数据。改进了对冷凝物质系统的性质的预测。通过指定所需的特征来设计材料。对神经网络嵌入和操纵数据的本质进行设计材料。如何与量子机器学习和新的激活功能相交，以改善量子的机械转换效率。这应该允许一种迭代方法来建模凝结物质系统。