Advanced Neural Network Methods for Atomistic Materials Chemistry

Machine Learned Potentials (MLPs) are becoming increasingly attractive tools with which to simulate both molecules and materials; in simple domains, their accuracy has converged to that of DFT methods, while their computational cost is orders of magnitudes lower. This has allowed thorough investigation into larger systems and for longer timescales, all at near chemical accuracy. It is on these time- and length-scales that many microscopic processes which are relevant for materials properties take place. The generality of individual MLPs is also improving. However, they are still difficult to develop for complex, reactive and multicomponent materials. In order to simulate and, more importantly, understand catalytic processes, new methodologies are therefore required. This project will seek to adapt techniques from computer vision and natural language processing for use in the chemical domain. Concretely, recent advances in transfer learning (TL) methodologies will be explored; only a limited set of TL techniques have been used in the chemical literature to date, predominantly with a focus on molecular datasets. This research will seek to apply more recent TL techniques with the goal of improving the accuracy, efficiency of training and generalisability of MLPs. With an aim to improve understanding of catalytic processes by creating powerful MLPs, the project is expected to lead to methods and research data which will be made available to the wider computational chemistry and materials science research community. Creating MLPs for complex and reactive systems is challenging. This project will develop a new approach to training MLPs. These approaches will then be applied to study atomic-scale
catalytic processes, helping to further understand the dynamics of these systems. This project falls within the EPSRC "Computational and theoretical chemistry" and "Catalysis" research areas, under the "Physical sciences" theme. The project's aims align with this theme's stated strategy of meeting "the many societal and economic challenges that rely on fundamental science for solutions". With regards to the first research area, this project is of clear fundamental nature: it will lead to the development of new computational methodology with which to describe systems at an atomic scale (with an expectation to address fundamental questions about the "learning" of atomistic properties in the process), and this methodology will then be used to study the dynamics of these systems over time. Additionally, by aiming to combine developments in the field of computer science with emerging applications in chemistry and material science, this project aligns with the area's key goal of reaching "across disciplines with research". With regards to the second research area, this project is expected to contribute, in the long term, to "structural and kinetic studies to understand catalytic mechanisms."