INPhINIT Incoming Fellowship – Multiscale modeling of Complex Oxide-Based Nanostructured Catalysts Under Reaction Conditions assisted by machine-learning
konstantin.neyman@icrea.cat
RESEARCH PRODUCT / RESEARCH GROUP
https://www.icrea.cat/Web/ScientificStaff/Konstantin-M-Neyman-292
Transition to a sustainable society requires designing new catalysts able to mediate the synthesis and consumption of fuels and pollution abatement more efficiently. Modeling, simulation, and machine-learning provide unprecedented mechanistic insight and allow screening vast materials spaces for new catalysts. However, one of the great challenges in theoretical catalysis and catalysis research, in general, remains unsolved – to characterize the complexity of catalytic materials under operating conditions.
One of the goals of this project is thus to tackle the characterization of operating catalysts by modeling operating nanostructured catalysts. In order to account for the effect of finite temperature, pressure and the ongoing chemical reactions, quantum mechanical calculations will be combined with concepts from thermodynamics and statistical mechanics. In particular, we will evaluate the stable states of different nanostructured oxides under reaction conditions, and the energy landscape of reactants and intermediates on them. Microkinetic models catalysts will thereby be constructed and analyzed. We will target different technologically and environmentally important reactions, such as CO2 reduction. Machine-learning methods will be developed and applied to facilitate the characterization of the targeted systems and their interactions with reactants, reducing the number of required quantum-mechanical calculations and enabling the screening of a larger number of systems.
Workplan:
- Obtain thermochemical parameters of targeted reactions on different nanostructured oxide substrates by performing ML-assisted quantum mechanical calculations.
- Construct and evaluate microkinetic models using both mean-field and kinetic Monte Carlo approaches, assessing their efficiency and reliability.
- Perform computational experiments to determine how the catalytic activity is affected by the substrate structure, concentration of undercoordinated sites and steps or defects.
We are seeking a highly motivated candidate to work as a PhD student at the Institute of Theoretical and Computational Chemistry of the University of Barcelona, IQTCUB (www.iqtc.ub.edu) under the supervision of ICREA Professor Konstantin Neyman and Dr. Albert Bruix. The PhD project is devoted to the computational investigation of complex nanostructured inorganic materials using a combination of methods based on quantum mechanics, global optimization algorithms, and multiscale modeling, building on recent advances of the group. The successful candidate will join an international team of researchers and be trained in both technical and soft (e.g. project management and communication) skills though regular supervisions and via group-funded participation in courses and summer-schools. Participation in at least one national and one international conference is highly encouraged, as well as carrying out research stays abroad to strengthen knowledge exchange and ties to collaborators. The PhD will generally involve three types of projects: applied theory, method development, and collaborations with experimentalists. The time dedicated to each type will depend on the candidate´s skillset and preference.
Requirements:
– A strong background in theoretical chemistry and/or physics, physical chemistry, or related fields.
– Experience or training in quantum mechanical methods.
– Programming skills are an important advantage.
– High motivation, curiosity, and ability to work collaboratively as part of an international research team.
Good oral and written communication skills in English.
OTHER RELEVANT WEBSITES
Google scholar profile of Dr. Albert Bruix
https://scholar.google.com/citations?hl=de&user=SP4cuyMAAAAJ
Google scholar profile of Prof. Konstantin Neyman
https://scholar.google.com/citations?user=GTuSYqcAAAAJ&hl=en