Searching for Next Generation Spintronic Materials Using Machine Learning

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、传感器和许多其他设备的性能和效率，还可以实现全新的**设备概念，如新的量子量子比特。由于可以应用原子组成、结构和合成技术的巨大组合，材料科学仍然具有极大的挑战性。指导研究是非常具有挑战性的。Lumant公司是一家加拿大公司，正在开发一种基于机器学习的人工智能Xaedra，它可以被训练来预测材料结构和**成分，从而产生理想的性能。为了实现这一点，必须向Xaedra提供大量与任何特定物理性质相关的**经过验证的实验数据和理论计算。这个**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。**具体地说，该项目将教Xaedra如何预测一种名为阻尼值的属性，该属性决定了在磁动力学变化期间**有多少能量被耗散。在实现实用的“自旋电子学”--一种旨在用磁力取代电子电荷来创造新型超高效计算机的计算方案--的过程中，最大限度地减少损耗至关重要。该项目的成功将极大地扩展Xaedra在预测磁性和动态性质方面的能力。它还将展示Xaedra作为一个非常活跃和商业上突出的材料科学研究领域的工具的价值。

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.