Modelling 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)
|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|
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 external 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 .
 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
 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
 Gudla, H., Zhang*, C. and Brandell, D. J. Phys. Chem. B, 2020, 124: 8124, DOI: 10.1021/acs.jpcb.0c05108
 Gudla, H., Shao, Y., Phunnarungsi, S., Brandell, D. and Zhang*, C. J. Phys. Chem. Lett., 2021, 12: 8460, DOI: 10.1021/acs.jpclett.1c02474
 Shao, Y., Gudla, H., Brandell, D. and Zhang*, C. J. Am. Chem. Soc., 2022, 144: 7583, DOI: 10.1021/jacs.2c02389
 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
 Knijff, L. and Zhang*, C. Mach. Learn.: Sci. Technol., 2021, 2: 03LT03, DOI: 10.1088/2632-2153/ac0123 (Letter)
 Shao†, Y., Andersson†, L., Knijff, L. and Zhang*, C. Electron. Struct., 2022, 4: 014012, DOI:10.1088/2516-1075/ac59ca (Invited paper)
 Dufils, T., Knjiff, L., Shao, Y. and Zhang*, C. 2023, arXiv:2303.15307, DOI: 10.48550/arXiv.2303.15307
|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||PhD student@DTU (Denmark)|
|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) 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."