Designing and developing sustainable battery materials with artificial intelligence

博士生将开发和设计一种环保的可充电电池。人工智能将根据现有的科学和工业数据来设计系统。非常希望使用可再充电电池来存储来自可再生源的能量以及在消费电子产品和汽车中。该市场由锂离子电池（LIB）主导，其具有高效率和长寿命周期。LIB价格昂贵，稀缺，回收它们对环境有很大的影响。利用逆向工程和深度学习，我们的目标是设计对环境影响最小的下一代电池。成功的候选人将联合收割机数据分析与深度学习方法相结合，并使用达勒姆最先进的制造设施进行实验。候选人将有机会开发一种新的模型，将电池的特性与材料的组成相关联。该模型还将详细考虑所有流程的环境影响。由于该项目有产业支持，候选人将与WeLoop公司密切合作，评估现有电池的生命周期评估，作为设计下一代电池的工具。要取得成功，广泛的技能是必不可少的，以及对实验，编码/模拟和实际工程的热情。

The PhD student will develop and design an eco-friendly rechargeable battery. Artificial Intelligence will be used to design the system based on existing scientific and industrial data. It is highly desirable to use rechargeable batteries to store energy from renewable sources and in consumer electronics and automobiles. This market is dominated by lithium-ion batteries (LIB) which have high efficiency and long life cycles. LIBs are expensive, scarce, and recycling them has a large environmental impact. Using inverse engineering and deep learning, we aim to design next-generation batteries with minimal environmental impact. The successful candidate will combine data analyses with Deep Learning methods and experiment with the state-of-the-art fabrication facilities available at Durham. The candidate will have the opportunity to develop a novel model that correlates the properties of batteries to the composition of materials. This model will also consider the environmental impact of all processes in detail. Because the project has industrial support, the candidate will work closely with the company (WeLoop) to assess the life cycle assessment of existing batteries as a tool to design the next generation batteries. To be successful, a broad skill set is essential, as well as an enthusiasm for experimentation, coding/simulation, and practical engineering.