TeC group @ UU


Modeling electrochemical systems with atomistic simulation and ML

We are a computational group based in Uppsala,  
working on "The physical chemistry of ionically  
conducting solutions" and "The physical chemistry  
of electrically charged interfaces" for energy  
storage applications.


(From left to right: Yunqi, Lisanne, Linnéa, Omar, Yong and Chao)

People :cn: :india: :netherlands: :sweden: :fr:

Name Role Email Note
Chao Zhang PI chao.zhang@TeC Biosketch
Yunqi Shao PhD student yunqi.shao@TeC Liquid and polymer electrolytes
Harish Gudla Postdoc harish.gudla@TeC Self-healing polymers (with Kristina Edström)
Lisanne Knijff PhD student lisanne.knijff@TeC Oxide/electrolyte interfaces
Linnéa Andersson PhD student linnea.andersson@TeC Metal/electrolyte interfaces
Thomas Dufils Postdoc thomas.dufils@TeC Graphene-oxide/electrolyte interfaces
Aishwarya Sudhama PhD student aishwarya.sudhama@TeC Oxide/electrolyte interfaces
Alicia van Hees Master student TBA Solution electrochemistry

:warning: Replace “TeC” with “kemi.uu.se”

Research directions

:dart: Modeling electrochemical interfaces with finite field MD

A realistic representation of an electrochemical interface requires treating electronic, structural, and dynamic properties on an equal footing. Density-functional theory-based molecular dynamics (DFTMD) method is perhaps the only approach that can provide a consistent atomistic description. However, the challenge for DFTMD modeling 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 in this area is the development of 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).

:dart: Simulating charge transport in battery electrolytes

Lithium batteries are electrochemical devices that involve multiple time-scale and length-scale to achieve their 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 rational design.

Currently, we are working on MD simulations of ionic conductivity and other transport coefficients, e.g. transference number, in different types of electrolytes from aqueous electrolytes to polymer electrolytes (with Daniel Brandell) which are relevant to battery applications [4-6].

:dart: Developing atomistic machine learning for electrochemistry

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 physicochemical 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 for predicting potential energy surface, polarization, and response charge (to the external bias) [7-10], and demonstrate how the chemical insight “learned” by such a network can be extracted. This will allow us to carry out the atomistic simulation of electrochemical systems powered by ML models.

Recent publications

[1] Zhang*, C., Sayer. T., Hutter, J. and Sprik, M. J. Phys.: Energy, 2020, 2: 032005, DOI:10.1088/2515-7655/ab9d8c (Topical Review)

[2] Jia, M., Zhang*, C. and Cheng*, J. J. Phys. Chem. Lett., 2021, 12: 4616, DOI: 10.1021/acs.jpclett.1c00775

[3] Knijff, L., Jia, M. and Zhang*, C. 2023, Reference Module in Chemistry, Molecular Sciences and Chemical Engineering, DOI: 10.1016/B978-0-323-85669-0.00012-X

[4] Gudla, H., Zhang*, C. and Brandell, D. J. Phys. Chem. B, 2020, 124: 8124, DOI: 10.1021/acs.jpcb.0c05108

[5] Gudla, H., Shao, Y., Phunnarungsi, S., Brandell, D. and Zhang*, C. J. Phys. Chem. Lett., 2021, 12: 8460, DOI: 10.1021/acs.jpclett.1c02474

[6] Shao, Y., Gudla, H., Brandell, D. and Zhang*, C. J. Am. Chem. Soc., 2022, 144: 7583, DOI: 10.1021/jacs.2c02389

[7] Shao, Y., Knijff, L., Dietrich, F. M., Hermansson, K. and Zhang*, C. Batter. Supercaps, 2021, 4: 585, DOI:10.1002/batt.202000262 (Minireview)

[8] 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

[9] Knijff, L. and Zhang*, C. Mach. Learn.: Sci. Technol., 2021, 2: 03LT03, DOI: 10.1088/2632-2153/ac0123 (Letter)

[10] Shao, Y., Andersson, L., Knijff, L. and Zhang*, C. Electron. Struct., 2022, 4: 014012, DOI:10.1088/2516-1075/ac59ca (Invited paper)


Name Country Role Current Position/Location
Harish Gudla :india: PhD student (with Daniel Brandell) Postdoc@UU (Sweden)
Alexandros Zantis :cyprus: Summer intern Master student@UU (Sweden)
Albert Pettersson :sweden: Master student Chemical engineer@Sandvik Materials Technology (Sweden)
Yong Li :cn: Postdoc Assistant professor@CQU (China)
Majid Shahbabaei :iran: Postdoc Assistant professor@BNUT (Iran)
Omar Malik :united_arab_emirates: Summer intern Master student@UU (Sweden)
Linnéa Andersson :sweden: Research assistant PhD student@UU (Sweden)
Florian Dietrich :de: Master student PhD student@UCL (UK)
Mei Jia :cn: Visiting PhD student Lecturer@SQNU (China)
Lisanne Knijff :netherlands: Master student PhD student@UU (Sweden)
Supho Phunnarungsi :thailand: Summer intern Bangkok (Thailand)
Shuang Han :cn: Master student PhD student@DTU (Denmark)
Are Yllö :sweden: Master student Research engineer@Sandvik Coromant (Sweden)
Thomas Sayer :uk: Visiting PhD student Postdoc@CUBoulder (US)


We thank the financial supports from Vetenskapsrådet (VR), the European Research Council (ERC) and the Wallenberg Initiative Materials Science for Sustainability (WISE). We are also part of the materials modelling community TEOROO based at Kemi-Ångström.


"The drive for excellence relies on both competitive 
energy and collaborative partnership."