Deep Neural Networks for Real-Time Spectroscopic Analysis

科学突破往往与技术发展密切相关，这使得对物质的测量变得更加详细。在钻石光源和欧洲X射线自由电子激光等新一代高亮度光源的变革效应以及实验室X射线光谱仪的出现的推动下，X射线光谱学(XS)正在发生一场现代革命。除了仪器和方法的发展，X射线吸收(XAS)和(非)共振发射(XES和RXES/RIXS)光谱学的进步正在对自然科学产生深远的影响。然而，这些新类型的实验，以及它们不断提高的分辨率和数据获取速度，让人们敏锐地关注到了一个新的挑战：我们如何高效而准确地分析这些数据，以确保能够提取出每个光谱中编码的有价值的定量信息？XS的高信息含量要求详细的理论处理，以将光谱可观测性与潜在的几何、电子和自旋结构联系起来。然而，这绝不是一项微不足道的任务。一个最好的例子是在无序体系的X-射线光谱分析中，例如在OPANDO催化剂中，其中的光谱代表从许多不等价的吸收位记录的平均信号。必须对系统的无序性进行建模，以便进行定量分析，但从理论上处理每个可能的化学不等价吸收部位(甚至采样大量这样的部位)在计算上是具有挑战性的、资源密集型和耗时的。它目前对大多数XS终端用户来说是遥不可及的，对于最复杂的系统，甚至是专家理论家也是如此。此外，终端用户并不总是显而易见的：a)如何应用最合适的理论处理方法，或b)他们的应用程序可能从数据中获得更多的洞察力。因此，现状是严重依赖于经验规则，例如根据氧化态调整吸收边位置，或者收集参考光谱并使用这些参考光谱的线性组合来拟合吸收轮廓。只要这一现状不受挑战，许多XS实验除了指纹识别外仍然有用，大量有价值的定量信息没有被利用，最终限制了我们的理解。该奖学金提案的目标是开发并随后为研究人员提供易于使用、计算廉价和可访问的工具，用于快速和自动地分析和预测XS。我们将优化和部署深度神经网络(DNN)，能够为任意吸收部位提供XS的瞬时预测，从而使XS数据分析工作流程的易用性和准确性发生阶段性变化。使用DNNS，可以将预测XS数据所需的时间从几小时/天减少到几秒钟，使数据分析大众化，为开发新的高通量XS实验打开大门，并通过促进对XS数据的实时分析/诊断，允许最终用户计划和更好地利用他们的波束时间分配。

Scientific breakthroughs are often strongly associated with technological developments, which enable the measurement of matter to an increased level of detail. A modern revolution is underway in X-ray spectroscopy (XS), driven by the transformative effect of next-generation, high-brilliance light sources e.g. Diamond Light Source and the European X-ray Free Electron Laser and the emergence of laboratory-based X-ray spectrometers. Alongside instrumental and methodological developments, the advances enabled in X-ray absorption (XAS) and (non-)resonant emission (XES and RXES/RIXS) spectroscopies are having far-reaching effects across the natural sciences. However, these new kinds of experiments, and their ever-higher resolution and data acquisition rates, have brought acutely into focus a new challenge: How do we efficiently and accurately analyse these data to ensure that valuable quantitative information encoded in each spectrum can be extracted?The high information content of an XS, demands detailed theoretical treatments to link the spectroscopic observables to the underlying geometric, electronic and spin structure. However, this is a far from trivial task. A prime example is found in the XS of disordered systems, e.g. in operando catalysts, in which the spectrum represents an average signal recorded from many inequivalent absorption sites. The disorder of the system must be modelled for a quantitative analysis, but to treat theoretically every possible chemically inequivalent absorption site (or even to sample a meaningful number of such sites) is computationally challenging, resource-intensive, and time-consuming. It is presently out of reach for the majority of XS end-users and, for the most complex systems, even expert theoreticians. To add to this, it is not always apparent to end-users: a) how to apply the most appropriate theoretical treatments, or b) where more insight might be attainable from the data by their application. Consequently, the status quo is to rely heavily on empirical rules, e.g. the scaling of absorption edge position with oxidation state, or to collect reference spectra and use linear combinations of these to fit the absorption profile. As long as this status quo is unchallenged, the many XS experiments remain useful for little more than fingerprinting, and a wealth of valuable quantitative information is left unexploited, ultimately limiting our understanding.The objective of this fellowship proposal is to develop and subsequently equip researchers with easy-to-use, computationally inexpensive, and accessible tools for the fast and automated analysis and prediction of XS. We will optimize and deploy deep neural networks (DNNs) capable of providing instantaneous predictions of XS for arbitrary absorption sites, introducing a step change in ease and accuracy of the XS data analysis workflow. Using DNNs, it is possible to reduce the time taken to predict XS data from hours/days to seconds, democratise data analysis, open the door to the development of new high-throughput XS experiments, and allow end users to plan and utilise better their beamtime allocations by facilitating on-the-fly 'real-time' analysis/diagnostics for XS data.