Project summary: Heterogeneous catalysis is heavily used in industry and is pivotal in our efforts to make chemical processes greener and more efficient. Direct experimental identification of the catalytically active sites and reaction mechanisms in heterogenous catalysts is challenging, hindering the ability to design catalysts for application [1]. Therefore, the objectives of the project are: 

  1. Develop machine learning potentials (MLPs), that will accelerate exploration of catalyst configurational space by few orders of magnitude while retaining ab initio accuracy. In addition, the MLPs can be used as a surrogate model enhancing potential energy space sampling using ab initio method, allowing for continuous improvement of MLP via delta- and active-learning strategies. 
  2. Develop data-driven bias-free dimensionality reduction schemes in which the good collective variables accelerating rare events, i.e., chemical reactions, will be selected automatically. It will enable routine open-ended searches of complex (trans)formation in catalytic systems resolving metastable configurations and overcoming the barriers between them, which are both typically inaccessible to experimental probing. 

The successful candidate will be able to build on accumulated knowledge base in our group both with respect to development of machine learning potentials and generation of data-driven collective variables [2-4].

The grand application challenge will be the realistic atomic-level modelling of zeolite synthesis. By charting the configurational space of zeolite formation process the successful applicant will open new routes to novel or improved zeolitic materials by modifying existing synthetic procedures.

Profile of a successful candidate: 

Required - MSc. or equivalent in Chemistry, Physics, Material Science or a related field; good knowledge of English; experience in programming (ideally Python or similar)

Advantageous, but not required - background in Machine Learning, Statistics, Statistical Mechanics and Quantum Chemistry/Physics; experience with molecular simulations, high-performance computing (including GPU accelerated one) and Linux. 

Relevant publications of the project leader:

[1] Grajciar, L., Heard, C.J., Bondarenko, A.A., Polynski, M.V., Meeprasert, J., Pidko, E.A., Nachtigall, P. (2018): Towards Operando Computational Modeling in Heterogeneous Catalysis. In: Chem. Soc. Rev., 22, 8307-8348 

[2] Erlebach, A., Nachtigall, P., Grajciar, L., (2022): Accurate large-scale simulations of siliceous zeolites by neural network potentials. In: npj Comput. Mater., 8, 174.

[3] Saha, I., Erlebach, A., Nachtigall, P., Heard, C.J., Grajciar, L., (2022): Reactive Neural Network Potential for Aluminosilicate Zeolites and Water. In: ChemRxiv,

[4] Sipka, M., Erlebach, A., Grajciar, L., (2022): Understanding chemical reactions via variational autoencoder and atomic representations. In: J Chem. Theory Comput. (under revision), 2022,

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