My group focuses on developing finite-field methods in computational electrochemistry as well as multi-scale modelling of electrolyte materials and electrified solid-liquid interfaces in energy storage/conversion.
Drop me a line if you are interested in a summer internship, a thesis work (Master/PhD) or a research project (Postdoc). We are funded by Vetenskapsrådet (VR) and the European Research Council (ERC).
Nyckelord: density functional theory molecular dynamics solid state batteries electrolytes charged water interfaces atomistic machine learning
A respectable senior colleague once told me that a person doing Computational Electrochemistry needs to know four things (five to include Machine Learning): electronic structure theory, statistical mechanics, classical electrodynamics and chemical thermodynamics. This puts the density functional theory based molecular dynamics (DFTMD) as the method of choice. To me, DFTMD is not just a method but a spirit (or a bridge) which connects the "hard" world (solid state/surface science community) and "soft" world (soft matter/liquid state theory community).
Modelling electrochemical interfaces with finite field MD
A realistic representation of an electrochemical interface requires treating electronic, structural and dynamic properties on an equal footing. DFTMD method is perhaps the only approach that can provide a consistent atomistic description. However, the challenge for DFTMD modelling of material’s interfacial dielectrics is the slow convergence of the polarization P, where P is a central quantity to connect all dielectric properties of an interface.
Our contribution is to develop finite field MD simulation techniques for computing electrical properties (such as the dielectric constant of polar liquids and the Helmholtz capacitance of solid-electrolyte interfaces) [1-3]. Its DFTMD implementation is available in one of our community codes CP2K (www.cp2k.org).
Simulating charge transportation in battery electrolytes
Lithium batteries are electrochemical devices which involve multiple time-scale and length-scale to achieve its optimal performance and safety requirement. In terms of the electrolyte which serves as the ionic conductor, a molecular-level understanding of the corresponding transport phenomenon is crucial for the rational design.
Currently, we are working on MD simulations of ionic conductivities in different types of electrolytes from aqueous electrolytes to polymer electrolytes (with Daniel Brandell) which are relevant to battery applications [4,5].
Developing atomistic machine learning for materials modelling
Machine learning (ML) is becoming increasingly important in computational chemistry and materials discovery. Atomic neural networks (ANN), which constitute a class of ML methods, have been very successful in predicting physico-chemical properties and approximating potential energy surfaces.
Recently, we have taken the initiative and developed an open-source Python library named PiNN (https://github.com/Teoroo-CMC/PiNN/), allowing researchers to easily develop and train state-of-the-art ANN architectures specifically for making chemical predictions. In particular, we have designed and implemented an interpretable and high-performing graph convolutional neural network architecture PiNet [6-8], and demonstrate how the chemical insight “learned” by such a network can be extracted.
 Zhang, C., Hutter, J. and Sprik, M. J. Phys. Chem. Lett., 2019, 10: 3871, DOI:10.1021/acs.jpclett.9b01355
 Zhang, C., Sayer. T., Hutter, J. and Sprik, M. J. Phys.: Energy, 2020, 2: 032005, DOI:10.1088/2515-7655/ab9d8c (Topical Review)
 Jia, M., Zhang, C. and Cheng, J. J. Phys. Chem. Lett., 2021, 12: 4616, DOI: 10.1021/acs.jpclett.1c00775
 Shao, Y., Hellström, M., Yllö A., Mindemark, J., Hermansson, K., Behler, J. and Zhang, C. Phys. Chem. Chem. Phys., 2020, 22: 10426, DOI: 10.1039/C9CP06479F (2020 HOT PCCP article)
 Gudla, H., Zhang, C. and Brandell, D. J. Phys. Chem. B, 2020, 124: 8124, DOI: 10.1021/acs.jpcb.0c05108
 Shao, Y., Hellström, M., Mitev, P. D., Knijff, L. and Zhang, C. J. Chem. Inf. Model., 2020, 60: 1184, DOI: 10.1021/acs.jcim.9b00994
 Shao, Y., Knijff, L., Dietrich, F. M., Hermansson, K. and Zhang, C. Batter. Supercaps, 2021, 4: 585, DOI:10.1002/batt.202000262 (Minireview)
 Knijff, L. and Zhang, C. Mach. Learn.: Sci. Technol., 2021, DOI: 10.1088/2632-2153/ac0123 (Letter)
Kontakta katalogansvarig vid den aktuella organisationen (institution eller motsv.) för att rätta ev. felaktigheter.