Deep Neural Networks for Real-Time Spectroscopic Analysis

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.