Searching for Next Generation Spintronic Materials Using Machine Learning

使用机器学习寻找下一代自旋电子材料

• 批准号: 523137-2018
• 负责人: Burgess, Jacob
• 金额: $ 1.82万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=646542
• 关键词: Searching; Next; Generation; Spintronic; Materials

Engineering of materials is an increasingly important aspect of modern technology. Tuning physical material**properties offers incredible flexibility not only to optimize performance and efficiency of devices such as**computer transistors, solar cells, sensors, and many others, but also an avenue to realize fundamentally new**device concepts such as novel quantum qubits. Materials science remains extraordinarily challenging simply**due to the huge range of combinations of atomic composition, structure, and synthesis techniques that can be**applied. Guiding research is incredibly challenging. Lumiant Corporation is a Canadian company developing a**machine learning based artificial intelligence, Xaedra, that can be taught to predict material structures and**compositions that will yield desirable properties. To achieve this, Xaedra must be fed a large amount of**verified experimental data and theoretical calculations relating to any particular physical property. The**proposed project aims to explore Xaedra's applicability to predicting dynamic properties in magnetic materials.**Specifically, the project will teach Xaedra how to predict a property called damping which determines how**much energy is dissipated during a magnetodynamic change. Minimizing damping is crucial in the**actualization of practical 'spintronics' - a computing scheme that aims to use magnetism to replace electronic**charge to create a new breed of ultra-efficient computers. Success in this project will significantly expand**Xaedra's capabilities in predicting magnetic properties and dynamic properties. It will also demonstrate**Xaedra's value as a tool in a very active and commercially prominent area of materials science research.

材料工程是现代技术的一个日益重要的方面。调整物理材料 ** 属性提供了令人难以置信的灵活性，不仅可以优化计算机晶体管，太阳能电池，传感器等设备的性能和效率，而且还可以实现全新的设备概念，如新颖的量子比特。材料科学仍然是非常具有挑战性的，仅仅是由于原子组成，结构和合成技术的巨大范围的组合可以应用。指导研究是非常具有挑战性的。Lumiant公司是一家加拿大公司，开发了一种基于机器学习的人工智能Xaedra，可以被教导预测材料结构和成分，从而产生理想的性能。为了实现这一目标，Xaedra必须获得大量经过验证的实验数据和与任何特定物理性质相关的理论计算。** 拟议的项目旨在探索Xaedra在预测磁性材料动态特性方面的适用性。具体来说，该项目将教Xaedra如何预测一种称为阻尼的特性，这种特性决定了在磁动力学变化期间消耗了多少能量。最小化阻尼对于实现实用的“自旋电子学”至关重要--自旋电子学是一种旨在利用磁性取代电子电荷来创造新型超高效计算机的计算方案。该项目的成功将大大扩展 **Xaedra在预测磁特性和动态特性方面的能力。它还将展示 **Xaedra作为材料科学研究领域非常活跃和商业化的重要工具的价值。