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

Name	Role	Email	Note
Chao Zhang	PI	chao.zhang@TeC	Biosketch
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
Aishwarya Sudhama	PhD student	aishwarya.sudhama@TeC	Oxide/electrolyte interfaces
Alicia van Hees	PhD student	alicia.van-hees@TeC	Interfacial PCET
Jichen Li	PhD student	jichen.li@TeC	Polymer electrolytes (with Daniel Brandell)
Zhan-Yun Zhang	Postdoc	zhan-yun.zhang@TeC	WISE-ap1
Emily Azzopardi	Master student	N/A	Organic supercaps

Replace “TeC” with “kemi.uu.se”

Research directions

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).

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].

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 voltage bias) [7-9], 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 [10].

Recent publications

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

[2] Andersson, L., and Zhang^*, C. Curr. Opin. Electrochem., 2023, 42: 101407, DOI: doi.org/10.1016/j.coelec.2023.101407

[3] Knijff, L., Jia, M. and Zhang^*, C. Encyclopedia of Solid-Liquid Interfaces, 2024, 2: 567, DOI: 10.1016/B978-0-323-85669-0.00012-X

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

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

[6] Gudla, H., Edström, K., Zhang^*, C. arXiv:2311.08144, 2023, DOI: 10.48550/arXiv.2311.08144

[7] 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

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

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

[10] Dufils, T., Knjiff, L., Shao, Y. and Zhang^*, C. J. Chem. Theory Comput., 2023, 19: 5199, DOI: 10.1021/acs.jctc.3c00359

Alumni

Name	Country	Role	Current Position/Location
Yunqi Shao		PhD student	Research engineer@Chalmers (Sweden)
Thomas Dufils		Postdoc	Teacher@Lycée Albert Einstein (France)
Matthew Chagnot		Visiting PhD student	PhD student@NCSU (US)
Sheng Bi		Visiting postdoc	postdoc@Sorbonne (France)
Alicia van Hees		Master student	PhD student@UU (Sweden)
Harish Gudla		PhD student (with Daniel Brandell)	Postdoc@UU (Sweden)
Alexandros Zantis		Summer intern	Master student@UU (Sweden)
Albert Pettersson		Master student	Chemical engineer@Sandvik Materials Technology (Sweden)
Yong Li		Postdoc	Assistant professor@CQU (China)
Majid Shahbabaei		Postdoc	Assistant professor@BNUT (Iran)
Omar Malik		Summer intern	Master student@UU (Sweden)
Linnéa Andersson		Research assistant	PhD student@UU (Sweden)
Florian Dietrich		Master student	PhD student@UCL (UK)
Mei Jia		Visiting PhD student	Lecturer@SQNU (China)
Lisanne Knijff		Master student	PhD student@UU (Sweden)
Supho Phunnarungsi		Summer intern	Bangkok (Thailand)
Shuang Han		Master student	Postdoc@BASF (Germany)
Are Yllö		Master student	Research engineer@Sandvik Coromant (Sweden)
Thomas Sayer		Visiting PhD student	Postdoc@CUBoulder (US)

Acknowledgement

We thank the financial supports from Vetenskapsrådet (VR), the European Research Council (ERC) DeepProton project 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."