Deep Neural Networks for Real-Time Spectroscopic Analysis

科学突破往往与技术发展密切相关，技术发展使物质的测量更加精细。X射线光谱学（XS）正在发生一场现代革命，这是由下一代高亮度光源（如钻石光源和欧洲X射线自由电子激光器）的变革效应以及实验室X射线光谱仪的出现所推动的。随着仪器和方法的发展，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整个自然科学产生深远的影响。然而，这些新型实验及其更高的分辨率和数据采集速率，给我们带来了一个新的挑战：我们如何高效准确地分析这些数据，以确保可以提取编码在每个光谱中的有价值的定量信息？XS的高信息含量需要详细的理论处理，以将光谱观测值与潜在的几何，电子和自旋结构联系起来。然而，这是一个远不是微不足道的任务。一个最好的例子是在无序体系的XS中，例如在操作催化剂中，其中光谱表示从许多不等价吸收位点记录的平均信号。系统的无序必须被建模用于定量分析，但是理论上处理每一个可能的化学上不等价的吸收位点（或者甚至对有意义数量的这样的位点进行采样）在计算上是具有挑战性的、资源密集的和耗时的。它目前对于大多数XS最终用户来说是遥不可及的，对于最复杂的系统，甚至专家理论家也是如此。除此之外，最终用户并不总是清楚：a）如何应用最适当的理论处理，或者B）通过应用程序可以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，例如吸收边缘位置与氧化态的比例，或者收集参考光谱并使用这些光谱的线性组合来拟合吸收曲线。只要这一现状不受挑战，许多XS实验仍然只对指纹有用，并且大量有价值的定量信息未被利用，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，计算成本低廉，易于访问的工具，用于快速自动分析和预测XS。我们将优化和部署深度神经网络（DNN），能够为任意吸收位点提供XS的即时预测，从而在XS数据分析工作流程的易用性和准确性方面引入阶跃变化。使用DNN，可以将预测XS数据所需的时间从数小时/天减少到数秒，使数据分析民主化，为开发新的高通量XS实验打开大门，并允许最终用户通过促进XS数据的实时“实时”分析/诊断来更好地规划和利用其波束时间分配。

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.