open positions

Koordinátor/ka náboru a administrativní pracovník/pracovnice sekretariátu katedry

Development of MXene-Based Hybrid Catalysts for Efficient Photo- electrochemical Hydrogen Production

This PhD research aims at the development of novel hybrid catalysts for hydrogen production through photo-electrochemical (PEC) water splitting, emphasizing innovative, cost-effective materials and novel composite architectures. The work will focus on exploring MXenes (2D transition metal carbides/nitrides) as non-precious metal co-catalysts in combination with photoactive materials like MOFs and perovskites. MXenes exhibit exceptional properties, including high electrical conductivity, rich surface chemistry, and tunable electronic structures, making them ideal for improving charge separation, minimizing electron-hole recombination, and serving as robust supports for catalytic nanoparticles. The project will investigate the synergistic effects of MXene-based composites. The PhD work includes the synthesis, modification, and detailed characterization of MXenes
using techniques such as X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) among others. The MXenes will be combined with other photoactive materials, requiring interface engineering, followed by comprehensive electrochemical characterization. The prepared hybrid materials will be tested for hydrogen production under photoelectrochemical conditions. This research will contribute to the development of stable, reactive, and environmentally friendly PEC systems for sustainable hydrogen production. The research will be supported by the
international M-ERA.NET project HYDRAGON (2025–2028).

Relevant publications:
Recent advancement in MXene based heterojunctions toward CO₂ photo-reduction and H₂ production applications: A review; FlatChem 44 (2024) 100620
MXene-based hybrid materials for electrochemical and photoelectrochemical H₂ generation; Journal of Energy Chemistry 93 (2024) 111–125

Stabilization of metal nanoparticles by defects of 2D zeolites

Mgr. Ph.D., Michal Mazur

Metal nanoparticles confined within zeolite pores exhibit enhanced catalytic activity, high sintering resistance, and uniform active sites distribution. However, their performance is often limited by diffusion constraints. A promising alternative is the emerging class of 2D zeolites, which combine the benefits of traditional zeolite confinement with improved accessibility. These materials, including colloidal suspensions of monolayers, have unique layered structures that enable stronger interactions between the zeolite support and metal nanoparticles. Importantly, 2D zeolites allow for precise silanol engineering, enabling property tuning to meet specific catalytic requirements.This project aims to synthesize a series of 2D zeolites (MWW, IPC, MFI, and FER) and systematically engineer their surface silanols to stabilize metal nanoparticles. It will develop strategies to control nanoparticle size and distribution for optimal catalytic performance while replacing expensive noble metals with cost-effective transition metals such as Ni, Cu, and Zn. This approach seeks to enhance catalytic efficiency while promoting sustainable and economically viable synthesis.Advanced characterization methods will be employed to study these materials. In-situ atomic force microscopy in scanning electron microscopy (AFM-in-SEM) will be utilised to analyse nanoparticle size and spatial distribution on 2D zeolite surfaces, while the use of in-situ heating high-resolution scanning transmission electron microscopy (HR-STEM) will assess the thermal stability of the nanoparticles.The catalysts will be tested in demanding high-temperature reactions (mostly catalytic hydrogenations), to evaluate their stability, activity, and performance. Ultimately, this research aims to establish a comprehensive framework for silanol defect engineering, advancing the design of economically viable and environmentally sustainable zeolite-supported metal catalysts.

Developing machine learning tools for reactive atomistic material modelling

RNDr., Ph.D., Lukaš Grajciar

Machine learning force fields (MLFFs):

- Generation of reference ab initio database of energy/force/stress data and/or data mining of thereof and/or combination of existing datasets via, e.g., multi-task learning (e.g., ala https://doi.org/10.26434/chemrxiv-2023-8n737)
- Training of machine learning force fields using deep neural networks (e.g., using equivariant message passing networks such as MACE)
- Fine-tuning the machine learning force fields via various active-learning methods (query-by-committee, Gaussian mixture models, uncertainty driven enhanced sampling ala https://arxiv.org/abs/2402.03753 or uncertainty attribution https://www.nature.com/articles/s41467-024-50407-9)
- Transfer-learning (e.g., via conformational sampling proposed here 10.1021/acs.jctc.4c00643) to extend the applicability of MLFFs to similar systems and delta-learning to improve the accuracy of the baseline MLFFs for the case/reaction of interest

Machine learning driven (tensorial) property prediction:

- Generation of reference ab initio database of tensorial (NMR tensors, Born effective charge tensors for IR) and/or data mining of thereof
- Training of ML-based predictors using deep neural networks and/or kernel ridge regression (gaussian process regression)
Advanced (ML-accelerated) structure and reaction sampling:
- development and deployment of tools for global structure search (e.g., ML-accelerated via Gaussian process regression such as here 10.1103/PhysRevLett.124.086102)
- acceleration and improvement of methods for reaction network search/mapping (e.g., via OPTIM package https://www-wales.ch.cam.ac.uk/OPTIM/)
- development and deployment of ML-based collective variables (e.g., using autoencoder joined with message passing neural network such as here 10.1021/acs.jctc.2c00729 or in PINES 10.1021/acs.jctc.3c00923s) for enhanced sampling molecular dynamics simulations for: i) specific reactions, ii) for complex reaction in (reactive) solvents, and iii) for phase transitions.
- development and deployment of advanced (biased) molecular dynamics and (hybrid) Monte Carlo (MC) schemes (e.g., OPES - on-the-fly probability enhanced sampling method, kinetic MC for reaction network modelling, transition matrix MC for adsorption modelling, etc.)

Testing systems and beyond:

- extensive library of in-group-thoroughly investigated application systems (zeolites, oxidic surfaces, embedded metal clusters, etc.) for testing/fine-tuning the methods
- an optionality to deploy the herein implemented/developed toolkit for a grand application challenge of modelling synthesis of zeolitic materials

Networking and collaboration:

MLFFs & ML-based property predictors:
R. Gomez-Bombarelli (MIT, US); Alin M. Elena (STFC, UK); A. Fortunelli (U Pisa, IT); A. Bartok-Partay (U Warwick, UK)
ML-accelerated structure and reaction sampling:
D. Wales (U Cambridge, UK); V. van Speybroeck (U Gent, BE); L. Bonati (IIT Genoa, IT), T. Bucko (U Komenskeho, SK)

Relevant Publications:

1. Nature Communications, 2024, doi: 10.1038/s41467-024-48609-2
2. npj Computational Materials, 2022, doi: 10.1038/s41524-022-00865-w
3. Digital Discovery, 2025, doi: 10.1039/D4DD00306C
4. Journal of Chemical Theory and Computation, 2023, doi: 10.1021/acs.jctc.2c00729
5. Proceedings of Machine Learning Research, 2023, doi: 10.48550/arXiv.2301.03480

Operando Modelling of porous catalytic nanomaterials via machine learning

Ph.D., Christopher James Heard

Application of cutting edge machine-learning based methods in support of quantum chemistry calculations for enhanced sampling and analysis of catalytic porous materials (zeolites, MOFs, silicate derivatives) - towards bottom-up design of functional porous nanomaterials. Training, optimization and use of class-specific, reactive ML interatomic forcefields, combined with spectral characterization (NMR, FTIR, quasi-elastic neutron scattering) and reactive modelling on realistic systems, in close collaboration with experimental synthesis, spectroscopy and catalysis: towards the solution of grand challenges in the field. Including-

a. Heteroatom distribution, speciation and host-guest dynamics in realistic zeolites
b. Defect formation, modulation and engineering in zeolites
c. Mechanistic analysis of zeolite synthesis and hydrolysis

a. Investigation of heteroatom distribution in zeolites - application of unbiased global structure optimization techniques, combined with enhanced dynamical sampling (both unbiased molecular dynamics and biased dynamics for diffusion/reaction processes) in industrially important complex zeolites. Determination of the role of conditions (temperature, defects, heteroatom coupling, solvent loading) on the energetics and spectral signatures. Development and use of ML tensorial property prediction models for full spectrum calculations and characterisation of samples. Analysis of acidity, solvent dynamics and structure-function relationships in zeolites. Verification alongside high-level quantum chemical calculations (RPA, CCSD, MP2) in collaboration with experts.

Relevant Publications:
1. Digital Discovery, 2025,10.1039/D4DD00306C [GL1]
2. Faraday Discussions, 2025,10.1039/d4fd00100a
3. Chemical Science, 2023,10.1039/D3SC02492J

b. Defect engineering - the overall goal is to tailor the formation, concentration and distribution of defects to control properties relevant to catalysis, including diffusivity inside the pore system, pinning of co-catalytic species (e.g. metal centres) and driving selective reactions. This goal requires an atomistic understanding of the formation processes of defects, the conditions which generate them, the mechanisms of their formation, the nature of the defects themselves, and means by which they affect catalytic properties. The selected candidate will utilise (and improve) ML-driven dimensionality reduction schemes for automatic reaction pathway generation and investigate reactive processes on zeolites/MOFs - beyond the limits of human chemical intuition.

Relevant Publications:
1. Chemistry of Materials, 2021, 10.1021/acs.chemmater.1c02799
2. Angewandte Chemie, 2023,https://doi.org/10.1002/anie.202306183

c. Zeolite synthesis and hydrolysis - the atomistic, mechanistic processes in zeolite synthesis are incompletely understood. A combination of direct dynamical simulation and kinetic modelling, alongside X-ray, NMR and neutron scattering experiments will aim to reproduce, under operando modelling conditions, the early stages of zeolite synthesis, alongside related systems (silica surfaces/gels) and the hydrolysis process that is used extensively in the ADOR scheme, for targeted generation of new zeolites from selected inter-zeolite structural transitions.

Relevant Publications:
1. Nature Communications, 2024, http://dx.doi.org/10.1038/s41467-024-48609-2
2. npj Computational Materials, 2022, https://doi.org/10.1038/s41524-022-00865-w

Collaborations
1. QENS - A. O'Malley (Bath)
2. Enhanced Sampling of in Porous Materials - V. van Speybroeck (Gent)
3. Defect Engineering and Characterization - J.A. van Bokhoven (ETH), M. Shamzhy/ J. Cejka (CU)
4. Synthesis and NMR - R. Morris and S. Ashbrook (St Andrews)
5. Novel Synthetic Pathways - M. Moliner (ITQ Valencia)
6. High level Quantum Chemical Calculations - J. Sauer (Berlin)
7. Zeolite synthesis modelling, ML methods - R. Gomez-Bombarelli (MIT)

Coarse-grained models for quantitative prediction of acid-base properties of peptides and proteins

Doc. RNDr., Ph.D., Peter Košovan

The main focus of this thesis is to develop coarse-grained force fields which enable quantitative prediction of the net charge of peptides and proteins at various pH. It builds on the previous work of our research group, where we have shown that very simple models can quantitatively predict net charge of model peptides [1] and their interactions with polyelectrolytes at various pH [2,3]. Simultaneously, we would like to develop a realistic coarse-grained representation of globular proteins, for modeling their solutions at high concentrations.[4] The goal is to augment the earlier models in order to more accurately describe hydrophobic and steric interactions and hydrogen bonding. The main tasks of the PhD candidate will include running coarse-grained simulations using Espresso or LAMMPS and all-atom simulations using Gromacs or LAMPPS.
Potential candidates are encouraged to contactpeter.kosovan@natur.cuni.czfor additional information.

References:

[1] Lunkad, R.; Murmiliuk, A.; Hebbeker, P.; Boublík, M.; Tošner, Z.; Štěpánek, M.; Košovan, P. Quantitative Prediction of Charge Regulation in Oligopeptides.Mol. Syst. Des. Eng.2021,6(2), 122–131.https://doi.org/10.1039/D0ME00147C.
[2] Pineda, S. P.; Staňo, R.; Murmiliuk, A.; Blanco, P. M.; Montes, P.; Tošner, Z.; Groborz, O.; Pánek, J.; Hrubý, M.; Štěpánek, M.; Košovan, P. Charge Regulation Triggers Condensation of Short Oligopeptides to Polyelectrolytes.JACS Au2024, jacsau.3c00668.https://doi.org/10.1021/jacsau.3c00668.
[3] Lunkad, R.; Barroso da Silva, F. L.; Košovan, P. Both Charge-Regulation and Charge-Patch Distribution Can Drive Adsorption on the Wrong Side of the Isoelectric Point.J. Am. Chem. Soc.2022,144(4), 1813–1825.https://doi.org/10.1021/jacs.1c11676.
[4] Pineda S, Blanco P, Staňo R, Košovan P. Patchy charge distribution affects the pH in protein solutions during dialysis. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-2fzdp

ML-assisted design and characterization of oxide-supported metal nano-catalysts

Ph.D., Christopher James Heard

Use of ML-accelerated (and quantum chemistry-supported) simulations for bottom-up design of metal/metal-oxide cluster-based supported nanocatalysts for sustainable chemistry applications.The grand challenge in the field of supported nanocatalysts is to design materials with the maximum catalytic activity/selectivity, using the least catalytic material, while maintaining stability against deactivation. Single atom and sub-nanometre metal particles on oxide supports are a promising class of system, but there are significant gaps in knowledge at the atomic scale. Costly electronic structure calculations do not allow for operando modelling of these systems, their evolution, or the full reaction landscape.We wish to know:· How do the clusters interact with the surface?· How can we control their motion and growth?· What effect do particle size and shape have on the particular catalytic applications?· How can we utilise the effects of alloying to generate better catalysts?· What is the best system for a particular green chemistry application?Systems of interest include late transition metal and coinage metal clusters (Determination of the influence of the cluster composition, size and oxidation state, in addition to the interaction with (realistic models of) support, on nanocatalyst stability and activity will be a primary goal of the project, which will leverage ML to go far beyond the capacities of DFT and post-HF methods.The project will employ a variety of computational techniques, including ML-driven (surrogate model) unbiased global structure optimization, in combination with ML-enhanced atomistic dynamical simulations, for development of model systems. Discrete path sampling techniques for characterisation of the underlying energy landscape. Grand canonical Monte Carlo simulations will be deployed for the generation of metal/metal-oxo phase diagrams, to explain the complex oxidation behaviour at the sub-nanoscale. ML-assisted reaction path sampling techniques (OPES/variational autoencoders/umbrella sampling) will be employed to determine catalytic reaction mechanisms and transfer learning schemes will be used for the smooth transition between metals and the improvement of sampling efficiency across related reactive processes.Computational structural characterisation (alongside catalyst evolution) will be conducted alongside experimental verification by international collaborators, (e.g. LEED IV, STM, TEM, GISAXS, XPS and RAIRS), while reactivity calculations will support measurements of catalytic activity (e.g. TOF-MS).

Relevant Publications from the group:

Nanoscale, 2024,10.1039/D4NR00017J
Angewandte Chemie, 2022, https://doi.org/10.1002/anie.202213361
Catal. Sci. Technol., 2022,10.1039/D1CY02270A
Angewandte Chemie, 2020,https://doi.org/10.1002/anie.202015138
ACS Catalysis, 2020, 10.1021/acscatal.0c01344
J. Phys. Chem. C, 2017, 10.1021/acs.jpcc.6b12072
ACS Catalysis, 2016, 10.1021/acscatal.5b02708

Ongoing collaborations relevant to the project

D.J. Wales (Cambridge)
J.C. Schoen (MPI-Stuttgart)
G. Parkinson (TU Wien)
A. Fortunelli (Pisa)
S. Vajda/F. Loi (Heyrovsky)
M. Mazur / J. Cejka (CU)

The role of acid-base equilibria in polyelectrolyte complexation and biomolecular condensation

Doc. RNDr., Ph.D., Peter Košovan

Electrostatic interactions are one of the main driving forces of both, complexation of synthetic polyelectrolytes, and formation of biomolecular condensates. The net charge of these molecules is determined by the pH, therefore it is one of the key factors setting the conditions for condensation. Furthermore, a change in the pH can affect the charge on complex solutes, thereby affecting their partitioning between the condensate and the supernatant solution.[1,2] This project builds on our previous modeling of polyelectrolyte-based coacervates [3], using the grand-reaction method previously developed by our group.[4] The main goal of this thesis is to predict the phase stability and update of solutes in condensates formed by synthetic polyelectrolytes, peptides or proteins. The main tasks of the PhD candidate will include running coarse-grained simulations using Espresso or LAMMPS and all-atom simulations using Gromacs or LAMPPS.Potential candidates are encouraged tocontact peter.kosovan@natur.cuni.cz for additional information.

References:

[1] van Lente, J. J.; Claessens, M. M. A. E.; Lindhoud, S. Charge-Based Separation of Proteins Using Polyelectrolyte Complexes as Models for Membraneless Organelles.Biomacromolecules2019,20(10), 3696–3703.https://doi.org/10.1021/acs.biomac.9b00701.
[2] Staňo, R.; Van Lente, J. J.; Lindhoud, S.; Košovan, P. Sequestration of Small Ions and Weak Acids and Bases by a Polyelectrolyte Complex Studied by Simulation and Experiment.Macromolecules2024,57(3), 1383–1398.https://doi.org/10.1021/acs.macromol.3c01209.
[3] Landsgesell, J.; Hebbeker, P.; Rud, O.; Lunkad, R.; Košovan, P.; Holm, C. Grand-Reaction Method for Simulations of Ionization Equilibria Coupled to Ion Partitioning. Macromolecules 2020, 53, 3007–3020

PhD fellow in "Stabilization of metal clusters on the zeolite supports and their assessment by in-situ electron microscopy methods"

Mgr. Ph.D., Michal Mazur

Project summary:

Metal nanoparticles immobilized at zeolite supports are important class of heterogenous catalysts for hydrogenation and dehydrogenation, reforming, and water-gas shift reactions. Zeolites, porous elementosilicates, are used to encapsulate metal nanoparticles to improve their stability. Currently, in-situ encapsulation and post-synthesis functionalization are two main synthesis strategies for metal incorporation in zeolites. Zeolites are often prepared as 3D solids; however, some topologies have layered representatives. Direct exfoliation of zeolites into suspension of monolayers has widened the spectrum of novel zeolitic supports for functionalization, thus design of new catalysts.

The main objective of the thesis will be:

preparation of zeolites, including layered precursors;
functionalization of prepared supports with metal clusters;
characterization of prepared materials by advanced, in-situ electron microscopy techniques;
optimizing the synthesis procedure to achieve better stability of prepared catalysts; and
catalytic tests of prepared materials and investigation of metal stability under reaction conditions.

We are looking for motivated students with experience in synthesis and heterogenous catalysis.

The experience in transmission electron microscopy will be an advantage.

Machine learning force fields (MLFFs):

Machine learning driven (tensorial) property prediction:

Testing systems and beyond:

Networking and collaboration:

Relevant Publications:

Physchem.cz

Open Positions

Open postdoc and PhD positions

Koordinátor/ka náboru a administrativní pracovník/pracovnice sekretariátu katedry

Development of MXene-Based Hybrid Catalysts for Efficient Photo- electrochemical Hydrogen Production

Stabilization of metal nanoparticles by defects of 2D zeolites

Ionization of weak polyelectrolytes in the semidilute and entangled regimes

Theoretical study of chemical reaction mechanisms

Design of nanomaterials by concerted structural and chemical modification of labile precursors

Design of advanced catalytic materials by top-down construction and crystal structure nano-architectonics

Functional Polymers and Boron-Based Nanostructures for Biomedical Applications

Synthesis of smart polymer-coated nanomaterials for Pickering interfacial catalysis

Synthesis of advanced stimuli-responsive polymers for biomedical applications

Development of conjugated hyper-cross-linked porous organic polymers for sorption and catalytic applications

Polymeric materials with covalently attached anionic boron clusters

Modeling of the Gibbs-Donnan equilibrium in protein solutions

Ionization of weak polyelectrolytes in the semidilute and entangled regimes

Developing machine learning tools for reactive atomistic material modelling

Machine learning force fields (MLFFs):

Operando Modelling of porous catalytic nanomaterials via machine learning

Coarse-grained models for quantitative prediction of acid-base properties of peptides and proteins

ML-assisted design and characterization of oxide-supported metal nano-catalysts

The role of acid-base equilibria in polyelectrolyte complexation and biomolecular condensation

PhD fellow in "Stabilization of metal clusters on the zeolite supports and their assessment by in-situ electron microscopy methods"