100 BIG-MAP papers have been published (30/4-2024). All publications are listed below and in this list. Some papers reference KD's.


Publications 2024
  • A bridge between trust and control: Computational workflows meet automated battery cycling, P. Kraus, E. Bainglass, F.F. Ramirez, E. Svaluto-Ferro, L. Ercole, B. Kunz, S.P. Huber, N. Plainpan, N. Marzari, C. Battaglia, G. Pizzi, J. Mater. Chem. A, 2024. -This paper is the outcome of the Stakeholder initiative "Aurora" and demonstrates the full integration of Empa's robotic platform Aurora (Corsin Battaglia's lab) with the AiiDA workflow engine developed at EPFL and PSI. Full digital twins of battery samples are represented in AiiDA, and AiiDA can submit and retrieve fully automatically automated experiments of battery assembly and cycling. An advanced GUI (in the AiiDAlab platform) has also been developed, allowing to submit and analyse results from batches of experiments. (KD8, WP9)

  • Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials, S. Lee, S. Heinen, D. Khan, O.A. von Lilienfeld, Machine Learning: Science and Technology 5, 015052, 2024. - We propose an automated workflow extracting data from literature using ANN and ChatGPT. In a use case, we extract Oxidation Potentials (relevant for battery research). Furthermore, supervised machine learning models were trained to reach prediction errors similar to experimental uncertainties. The introduced pipeline significantly reduces human labor and exemplifies how to accelerate scientific research through automation.(WP11)
  • Unravelling degradation mechanisms and overpotential sources in aged and non-aged batteries: A non-invasive diagnosis, W.A. Appiah, L.H. Rieger, E. Flores, T. Vegge, A. Bhowmik, J. Energy Storage 84, 111000, 2024. -In this work, a physics-based model - Doyle-Fuller-Newman pseudo-2D framework - is proposed to unveil how different degradation processes affect the growth of SEI layer during battery cycling and how this affect cell lifetime. The obtained results are validated with cycling and EIS data from coin cell including LNO/Gr and NMC/Gr chemistries. The most representative result is that the model is able to estimate the interplay between the different degradation mechanisms and how this affects the SEI layer. With this information we aim to stablish best practices for cell cycling conditions looking for cycle life optimization. (WP3, WP11)

Publications 2023

  • 2023 Roadmap on molecular modelling of electrochemical energy materials, C. Zhang, J. Cheng, Y. Chen, M.K.Y. Chan, Q. Cai, R.P. Carvalho, C.F.N. Marchiori, D. Brandell, C.M. Araujo, M. Chen, X. Ji, G. Feng, K. Goloviznina, A. Serva, M. Salanne, T. Mandai, T. Hosaka, M. Alhanash, P. Johansson, Y.-Z. Qiu, H. Xiao, M. Eikerling, R. Jinnouchi, M.M. Melander, G. Kastlunger, A. Bouzid, A. Pasquarello, S.-J. Shin, M.M. Kim, H. Kim, K. Schwarz, R. Sundararaman, J. Phys. Energy 5, 041501, 2023.

  • Blended-salt electrolyte design for advanced NMC811ǁGraphite cell performance, P. Yan, M. Shevchuk, C. Wölke, F. Pfeiffer, D. Berghus, M. Baghernejad, G.-V. Röschenthaler, M. Winter, I. Cekic-Laskovic, Small Structures, 2300425, 2023. - Highlights the synergistic effect of newly synthesized conducting salt LiDFTFSI and film-forming additive VC leading to the formation of effective SEI and CEI on corresponding electrodes and the significantly enhanced electrochemical performance of the resulting NMC811ǁ Graphite cell chemistry. (WP6)

  • Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design, K. Karandashev, J. Weinreich, S. Heinen, D.J. Arismendi Arrieta, G.F. von Rudorff, K. Hermansson, and O.A. von Lilienfeld, J. Chem. Theory Comput. 19, 8861–8870, 2023. - We propose an algorithm for optimization of organic molecules and demonstrate its efficiency for problems related to battery electrolyte component optimization. (WP2)
  • Graph neural network interatomic potential ensembles with calibrated aleatoric and epistemic uncertainty on energy and forces, J. Busk, M.N. Schmidt, O. Winther, T. Vegge, P.B. Jørgensen, Phys. Chem. Chem. Phys. 25, 25828-25837, 2023. -We present a complete framework for training and recalibrating graph neural network interatomic potential ensemble models to produce accurate predictions of energy and forces with calibrated uncertainty estimates. The proposed method considers both epistemic and aleatoric uncertainty and the total uncertainties are recalibrated post hoc using a nonlinear scaling function to achieve good calibration without loss of predictive accuracy. The method achieved low prediction error and good uncertainty calibration on two challenging datasets. (KD11, WP11)
  • How beam damage can skew synchrotron operando studies of batteries, T. Jousseaume, J.-F. Colin, M. Chandesris, S. Lyonnard, S. Tardif, ACS Energy Lett. 8, 8, 3323-3329, 2023. - Highlights the fictitious phase transitions that can be induced by synchrotron beam on LNO or NMC cathodes in cycling batteries, and provide quantification of the dose and dose rates where degradation occurs. (KD4, WP5)


Publications 2022


Publications 2021


Publications prior to start of funding period
30 MAY 2024