Microstructural fingerprint: The application of machine learning methods for the characterization and optimisation of electrode microstructures

为能源储存应用领域的机器学习领域的研究学生的申请被邀请，导致博士学位的授予。该职位由法拉第学院提供的助学金和学费(按英国/欧盟学生费率)支持。TFI集群博士生除了标准的EPSRC待遇外，还可以获得更高的津贴。每年的津贴总额约为20,000 GB(加上伦敦权重)，外加每年7,000 GB的额外津贴，以支付培训和旅行费用。获奖者将有机会获得多个网络机会、行业访问、指导、实习以及高质量的体验，从而进一步发展知识、技能和抱负。EPSRC候选人应符合该奖项的资格标准。请在以下网站上检查您的适合性：锂离子电池的http://www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspxThe性能与其多孔电极的3D微结构有关。微/纳米层析成像领域的进展使研究人员能够以与多物理模拟需要相关的分辨率捕捉这些微结构的形貌[1]；然而，对这些数据的稳健表征和分析仍然是一个挑战。机器学习的最新进展见证了新的图像生成工具的发展。特别是，这些方法包括使用分层神经体系结构的风格转移[2]、变分自动编码器[3]和对抗性方法[4]。这些概念在过去5年中在2D彩色图像的背景下得到了迅速发展，但很少被应用于3D标记微结构数据的生成。该项目将寻求将这些方法的力量转移到微结构分析和生成领域。首先，通过能够提取这些存储密集型3D断层摄影体的压缩表示(指纹)，然后使用这些表示来更有效地探索可能的微结构的空间，以找到新的最佳配置。这将与多尺度建模和退化快速启动项目中正在进行的重要层析成像调查相联系，并与寻求建立解释单元性能的简化模型的连续介质建模工作相互作用。

Applications are invited for a research studentship in the field of machine learning for energy storage applications leading to the award of a PhD degree. The post is supported by a bursary and fees (at the UK/EU student rate) provided by the The Faraday Institution. TFI Cluster PhD students receive an enhanced stipend over and above the standard EPSRC offer. The total annual stipend is approximately £20,000 (plus London weighting) plus an additional £7,000 annually to cover training and travel costs. Recipients will have access to multiple networking opportunities, industry visits, mentorship, internships, as well as quality experiences that will further develop knowledge, skills, and aspirations. EPSRC candidates should fulfil the eligibility criteria for the award. Please check your suitability at the following web site: http://www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspxThe performance of lithium ion batteries is linked to the 3D microstructure of their porous electrodes. Advances in the field of micro/nano-tomography have enabled researchers to capture the morphologies of these microstructure at a resolution relevant to needs of multiphysics simulation [1]; however, the robust characterisation and analysis of this data remains a challenge. Recent advances in machine learning have seen the development of novel image generation tools. In particular, these include style transfer using hierarchical neural architectures [2], variational autoencoders [3] and adversarial methods [4]. These concepts have been developing rapidly in the context of 2D colour images over the past 5 years but have rarely been applied to the generation of 3D labelled microstructural data. This project would seek to transfer the power of these methods to the field of microstructural analysis and generation. First by enabling the extraction of a compressed representations (a "fingerprint") of these memory intensive 3D tomography volumes and then using these representations to more efficiently explore the space of possible microstructure to find new optimal configurations. This will link up with the significant tomographic investigations underway in both the multiscale modelling and degradation fast-start projects, as well as interacting with the continuum modelling efforts seeking to build simplified models explaining cell performance.