Computational electrochemistry 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: Zhan-Yun, Aishwarya, Emily, Alicia, Lisanne, Aniello, Linnéa, and Jichen)
Name | Role | Note | |
---|---|---|---|
Chao Zhang | PI | chao.zhang@TeC | Biosketch |
Lisanne Knijff | PhD student | lisanne.knijff@TeC | ML-accelerated simulations |
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 |
Aniello Langella | Visiting PhD student | aniello.langella@unina.it | Metal/polymer interface (with Daniel Brandell) |
Replace “TeC” with “kemi.uu.se”
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).
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].
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].
[1] Andersson, L. and Zhang*, C. Curr. Opin. Electrochem., 2023, 42: 101407, DOI: doi.org/10.1016/j.coelec.2023.101407
[2] 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
[3] Andersson, L., Sprik, M., Hutter, J. and Zhang*, C. Angew. Chem. Int. Ed., 2024, e202413614, DOI: 10.1002/anie.202413614
[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. and Zhang*, C. ACS Mater. Au, 2024, 4: 300 DOI: 10.1021/acsmaterialsau.3c00098
[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
Name | Country | Role | Current Position/Location |
---|---|---|---|
Harish Gudla | Postdoc (with Kristina Edström) | Computational materials scientist@Compular (Sweden) | |
Emily Azzopardi | Master student | Uppsala (Sweden) | |
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) |
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."