Project summary:
In this project you will drive the development and application of machine learning methods towards simulations of catalytically important, zeolite-encapsulated single atom catalysts (SAC) and related sub-nanometre scale catalysts. You will identify catalyst design principles that will inform the next generation of heterogeneous catalytic materials.

Single atom catalysts[1] supported by encapsulation into zeolite pores are highly efficient, cost-effective catalytic systems, used in a rapidly growing number of industrially important processes, from environmental protection to fuel upgrading.[2] However, we are only recently beginning to understand how these catalysts behave on an atomistic scale. The mechanisms of migration, growth and redispersion are so far not well understood, nor is the nature of the interaction with the supporting material, even though these effects are very likely to influence reactivity and long-term stability.[3] This gap in understanding provides a great opportunity for ambitious researchers to tackle fundamental problems in catalysis via computational means.[4,5]

The ultimate goal is the development of a set of design principles to optimise the stability and functionality of SACs in industry.

The goals of the project are to:

a) Develop reactive machine learning potentials for the migration, sintering and metal-framework interactions of small clusters in zeolites.

b) Apply biased dynamical simulations to understand the dynamical processes at work in these systems at the atomistic scale.

c) Collaboration with experimentalists to aid in design of robust cluster systems for synthesis of practical sub-nanometre scale catalytic materials.

This project will include the development of computational methods, in particular, neural network-based machine learning potentials. These methods will be applied towards long-time simulations and statistical analysis of SAC binding, growth and migration processes. The successful candidate will gain experience in programming, simulation methods, maintaining local and international collaborations, and presentation at international conferences. 

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] Heterogeneous single-atom catalysis”, A. Wang, J. Li, and T. Zhang, (2018), Nature Reviews Chemistry, 2(6), 65–81. [2] 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. [3a] Origin of the Unusual Stability of Zeolite-Encapsulated Sub-Nanometer Platinum” D. Hou, L. Grajciar, P. Nachtigall and C. J. Heard, (2020), ACS Catalysis., 10, 11057–11068. [3b] Fast room temperature lability of aluminosilicate zeolites“, C.J Heard, L. Grajciar, C.M. Rice, S.M. Pugh, P. Nachtigall, S.E. Ashbrook, R.E. Morris, (2019), Nature Communications, 10 (1), 1-7. [3c] Migration of Zeolite-Encapsulated Pt and Au Under Reducing Atmospheres”, D. Hou and C.J Heard, ChemRxiv 10.26434/chemrxiv.14456316.v1 [4] “Kinetic regimes in ethylene hydrogenation over transition-metal surfaces” C.J Heard, C. Hu, M. Skoglundh, D. Creaser, H. Grönbeck, (2016), ACS Catalysis, 6(5), 3277-3286  [5a] 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. [5b] 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.

Current research grant of the project leader: GACR grant 20-26767Y: “Stability of Metal Particles Encapsulated in Zeolites: Multiscale Modelling and Experimental Benchmarking” (two postdocs and two PhD students) 2020-2022. Total budget approx.€ 250.000.

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