1) Giannis D. Savva, Raz L. Benson, Ilektra-Athanasia Christidi and Michail Stamatakis. 2023. Exact distributed kinetic Monte Carlo simulations for on-lattice chemical kinetics: lessons learnt from medium- and large-scale benchmarks. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2250): 20220235.

DOI: 10.1098/rsta.2022.0235

2) Giannis D. Savva, Raz L. Benson, Ilektra-Athanasia Christidi and Michail Stamatakis.2023. Large-scale benchmarks of the Time-Warp/Graph-Theoretical Kinetic Monte Carlo approach for distributed on-lattice simulations of catalytic kinetics. Physical Chemistry Chemical Physics, 25: 5468-5478.

DOI: 10.1039/D2CP04424B

3) Julia Schumann, Michail Stamatakis, Angelos Michaelides and Romain Réocreux.2022. Reactivity of Single-Atom Alloys as Easy as Counting to Ten. ChemRxiv.

DOI: 10.26434/chemrxiv-2022-d5hhf

4) Miguel Pineda and Michail Stamatakis. 2022. Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status and challenges. J. Chem. Phys. 156, 120902.

DOI: 10.1063/5.0083251

5) Daniele Micale, Claudio Ferroni, Riccardo Uglietti, Mauro Bracconi, Matteo Maestri.2022. Computational Fluid Dynamics of Reacting Flows at Surfaces: Methodologies and Applications. Chemie Ingenieur Technik.

DOI: 10.1002/cite.202100196

6) Alejandro Peña-Torres, Abid Ali, Michail Stamatakis, and Hannes Jónsson. 2022. Indirect mechanism of Au adatom diffusion on the Si(100) surface. Phys. Rev. B 105, 205411.

DOI: 10.1103/PhysRevB.105.205411

7) Joris Mollinga and Valeriu Codreanu. 2022. Scaling Out Transformer Models for Retrosynthesis on Supercomputers. In Intelligent Computing. Kohei Arai, Ed. Lecture Notes in Networks and Systems. Springer International Publishing, Cham, 102–117.

DOI: 10.1007/978-3-030-80119-9_4

8) Brett Pomeroy, Miha Grilc, and Blaž Likozar. 2022. Artificial neural networks for bio-based chemical production or biorefining: A review. Renewable and Sustainable Energy Reviews 153, 111748.

DOI: 10.1016/J.RSER.2021.111748

9) Srikanth Ravipati, Giannis D. Savva, Ilektra-Athanasia Christidi, Roland Guichard, Jens Nielsen, Romain Réocreux, and Michail Stamatakis. 2022. Coupling the time-warp algorithm with the graph-theoretical kinetic Monte Carlo framework for distributed simulations of heterogeneous catalysts. Computer Physics Communications 270, 4, 108148.

DOI: 10.1016/J.CPC.2021.108148

10) Aleksa Kojčinović, Žan Kovačič, Matej Huš, Blaž Likozar, and Miha Grilc. 2021. Furfural hydrogenation, hydrodeoxygenation and etherification over MoO2 and MoO3: A combined experimental and theoretical study. Applied Surface Science 543, 148836

DOI: 10.1016/j.apsusc.2020.148836

11) Daniele Micale, Riccardo Uglietti, Mauro Bracconi, and Matteo Maestri. 2021. Coupling Euler–Euler and Microkinetic Modeling for the Simulation of Fluidized Bed Reactors: an Application to the Oxidative Coupling of Methane. Ind. Eng. Chem. Res., 95.

DOI: 10.1021/acs.iecr.0c05845

12) Brett Pomeroy, M. Grilc, and B. Likozar. 2021. Process condition-based tuneable selective catalysis of hydroxymethylfurfural (HMF) hydrogenation reactions to aromatic, saturated cyclic and linear poly-functional alcohols over Ni–Ce/Al 2 O 3. Green Chem. 23, 20, 7996–8002.

DOI: 10.1039/D1GC02086B

13) Brett Pomeroy, Miha Grilc, Sašo Gyergyek, and Blaž Likozar. 2021. Catalyst structure-based hydroxymethylfurfural (HMF) hydrogenation mechanisms, activity and selectivity over Ni. Chemical Engineering Journal 412, 25, 127553.

DOI: 10.1016/j.cej.2020.127553

14) Žan Kovačič, Blaž Likozar, and Matej Huš. 2020. Photocatalytic CO 2 Reduction: A Review of Ab Initio Mechanism, Kinetics, and Multiscale Modeling Simulations. ACS Catal. 10, 24, 14984–15007.

DOI: 10.1021/acscatal.0c02557

15) Andraž Pavlišič, Matej Huš, Anže Prašnikar, and Blaž Likozar. 2020. Multiscale modelling of CO2 reduction to methanol over industrial Cu/ZnO/Al2O3 heterogeneous catalyst: Linking ab initio surface reaction kinetics with reactor fluid dynamics. Journal of Cleaner Production 275, 122958.

DOI: 10.1016/j.jclepro.2020.122958

16) Srikanth Ravipati, Mayeul d’Avezac, Jens Nielsen, James Hetherington, and Michail Stamatakis. 2020. A Caching Scheme To Accelerate Kinetic Monte Carlo Simulations of Catalytic Reactions. The journal of physical chemistry. A 124, 35, 7140–7154.

DOI: 10.1021/acs.jpca.0c03571 PubMed-ID: 32786994


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Fertigungstechnik und Angewandte
Materialforschung IFAM
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Adhäsions- und Grenzflächenforschung
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ReaxPro: Software Platform for Multiscale Modelling of Reactive Materials and Processes is a LEIT project in the call DT-NMBP-09-2018 – Accelerating the uptake of materials modelling software (IA), under grant agreement 814416.