Using Artificial Intelligence to Understand Zeolite Catalysts

Electron microscopy provides a unique tool for studying the local structure of materials at the atomic scale. However, a major challenge lies in correlating bulk measurements of properties with highly selective structural data. One approach is to take advantage of fast electron detectors to acquire large data sets of millions of images and to develop automated analysis tools based on deep learning to analyse these. The overall aim of this project is to study defect structures in zeolites involved in heterogeneous catalysis. This project will use recent developments in fast direct electron detectors for transmission electron microscopy for low dose imaging and neural networks trained for pattern recognition of specific defect structures. The potential impact of these studies is a better understanding of catalytic processes of interest to the industrial sponsor and an improved understanding of the relationships between catalytic performance and local structure.

Initially the project will use new detectors operating at kHz frame rates to record large datasets containing many TEM images of defect structures. In parallel the project will develop the use of machine learning based on convolution neural networks to build image analysis tools suitable for analysing large data sets containing millions of images. A convolutional neural network will be trained using simulated data of known defect structures for various electron dose budgets and other imaging conditions. This will then be used to analyse the experimental data to gain meaningful statistics on defect types. The research proposed relies heavily on unique instrumentation available at the electron Physical Sciences Imaging Centre. Specifically, a new high speed direct electron detector operating at a frame rate in excess of 2KHz in 12 bit counting mode will be used to acquire low dose data. Within all of the above aims and objectives it will be necessary to ensure that the methods developed are robust to low dose data acquisition as zeolites are known to be radiation sensitive and to ensure that electron beam induced effects are minimised. Initially pure zeolites will be studied but the project will also be extended to study metal loaded zeolites and comparisons between defects in these systems and the pure materials will provide insights into the mechanisms and structural consequences of metal loading. Finally catalytic data will be measured at the industrial sponsors laboratories to attempt to correlate catalytic performance with the nature and densities of defects present across a range of loaded and unloaded samples

The project falls within the EPSRC energy, Artificial Intelligence and Robotics and physical sciences research areas

The project is funded by Johnson Matthey plc through the iCase initiative.