Project summary:  
Introduction: Metal nanoparticles (MNPs) supported on metal oxide substrates are an important and growing class of nanocatalytic system, both in industry and from a fundamental perspective in academia.[1] Various factors, including particle shape, size, mobility and composition all affect the properties of MNPs. In addition, the local structure (e.g. termination, defects, charge) and chemical makeup of the support have a profound influence on catalytic activity and longevity.[2] These factors are complex, inter-related and are not well understood, due to a combination of experimental and computational limitations. In the former, the small size of MNPs, the high instability of samples, and non-inert nature of characterization techniques such as FTIR and TEM make unambiguous understanding of these particles difficult. In the latter, knowledge is hindered by the high computational expense of calculating the electronic structure of metals, a lack of proper sampling of possible structures, and the limited realism of model systems. This limitation can be lifted by acceleration of simulations via machine learning methods.[3]

Topic of Project: This project will focus on modelling the structure and nanocatalytic properties of metal nanoparticles (noble and coinage metals) in the 1 nm size range toward important classes of catalytic conversion. The effect of metal mixing will be considered, via the simulation of binary MNP alloys. The systems of interest will include oxide supports including simple ionic, hydroxylated and charged surfaces under realistic conditions, including defects. The successful candidate will gain experience in programming, simulation methods, maintaining local and international collaborations, and presentation at international conferences.

Methods: The project will include a combination of traditional electronic structure calculation methods (DFT) and the development of computational methods, in particular, reactive neural network-based machine learning potentials.[4] These methods will be applied towards long-time simulations and statistical analysis of particle structure and binding, screening of candidates, and calculation of reactive processes including particle migration, catalytic reactions and reconstructions. 

Features of a successful candidate: MSc. or equivalent in Chemistry, Physics or Materials Science, good knowledge of English (required), research background in molecular modelling, computational chemistry or solid-state physics (preferred). Experience with high performance computing and programming in a Linux environment (beneficial). 

[1]Recent advances in automotive catalysis for NOx emission control by small-pore microporous materials”, A. M. Beale, F. Gao, I. Lezcano-Gonzalez, C. H. F. Peden and J. Szanyi, (2015), Chem. Soc. Rev., 44, 7371–7405.

[2] 2D oxide nanomaterials to address the energy transition and catalysis”, C.J. Heard, J. Čejka, M. Opanasenko, P. Nachtigall, G. Centi, S. Perathoner, (2018), Advanced Materials, 31 (3), 1801712. [3] Towards operando computational modeling in heterogeneous catalysis”, L. Grajciar, C. J. Heard, A.A. Bondarenko, M.V. Polynski, J. Meeprasert, E.A. Pidko, P. Nachtigall, (2018), Chemical Society Reviews, 47 (22), 8307-8348 [4a]A. Erlebach, P. Nachtigall, L. Grajciar, npj Comput. Mater. 2022, 8, 174. [4b] I. Saha, A. Erlebach, P. Nachtigall, C. J. Heard, L. Grajciar, preprint: ChemRxiv, 2022,

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