Publications

Publications 2023

• Conductivity experiments for electrolyte formulations and their automated analysis, F. Rahmanian, M. Vogler, C. Wölke, P. Yan, S. Fuchs, M. Winter, I. Cekic-Laskovic, H.S. Stein, Sci. Data 10, 43, 2023. 

Dataset, visualization and data analysis with data lineage tracking are implemented in a software called MADAP

• OSSCAR, an open platform for collaborative development of computational tools for education in science, D. Du, T. J. Baird, S. Bonella, G. Pizzi, Computer Physics Communications 282, 108546, 2023.
• Sensitivity analysis methodology for battery degradation models, W. A. Appiah, J. Busk, T. Vegge, A. Bhowmik, Electrochim. Acta 439, 141430, 2023.
• Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory, L.H. Rieger, E. Flores, K.F. Nielsen, P. Norby, E. Ayerbe, O. Winther, T. Vegge , A. Bhowmik, Digital Discovery 2, 112, 2023.

Publications 2022

• Alchemical geometry relaxation, G. Domenichini, O. A. von Lilienfeld, J. Chem. Phys. 156, 184801, 2022.
• An orbital-based representation for accurate quantum machine learning, K. Karandashev, O. A. von Lilienfeld, J. Chem. Phys.156, 114101, 2022.
• Autonomous visual detection of defects from battery electrode manufacturing, N. Choudhary, H. Clever, R. Ludwigs, M. Rath, A. Gannouni, A. Schmetz, T. Hülsmann, J. Sawodny, L. Fischer, A. Kampker, J. Fleischer, H.S. Stein, Adv. Intell. Syst. 2200142, 2022.
• Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks, J. Busk, P.B. Jørgensen, A. Bhowmik, M.N. Schmidt, O. Winther, T. Vegge, Mach. Learn.: Sci. Technol. 3, 015012, 2022.
• Computationally efficient quasi-3D model of a secondary electrode particle for enhanced prediction capability of the porous electrode model, K. Zelič, T. Katrašnik, J. Electrochem. Soc. 169 040522, 2022.
• Data-driven analysis of high-throughput experiments on liquid battery electrolyte formulations: unraveling the impact of composition on conductivity, A.N. Krishnamoorthy, C. Wölke, D. Diddens, M. Maiti, Y. Mabrouk, P. Yan, M. Grünebaum, M. Winter, A. Heuer, I. Cekic-Laskovic, Chemistry - Methods e202200008, 2022.
• Designing electrode architectures to facilitate electrolyte infiltration for lithium-ion batteries, A. Shodiev, F.M. Zanotto, J. Yu, M. Chouchane., J. Li, A.A. Franco, Energy Storage Mater. 49, 268-277, 2022.
• Dynamic structure discovery applied to the ion transport in the ubiquitous Lithium-ion Battery electrolyte LP30, R. Andersson, O. Borodin, P. Johansson, J. Electrochem. Soc. 169 (10), 100540, 2022.
• Electrochemical Protocols to Assess the Effects of Dissolved Transition Metal in Graphite/LiNiO₂ Cells Performance, V. Meunier, M.L. De Souza, M. Morcrette, A. Grimaud, J. Electrochem. Soc. 169, 070506, 2022.
• Electrochemistry visualization tool to support the electrochemical analysis of batteries, M.L. de Souza, M. Duquesnoy, M. Morcrette,  A.A. Franco, Batteries & Supercaps, 2022.
• Enabling modular autonomous feedback-loops in materials science through hierarchical experimental laboratory automation and orchestration, F. Rahmanian, J. Flowers, D. Guevarra, M. Richter, M. Fichtner, P. Donnely, J.M. Gregoire, H.S. Stein, Adv. Mater. Interfaces, 2101987, 2022.
• Learning the laws of lithium-ion transport in electrolytes using symbolic regression, E. Flores, C. Wölke, P. Yan, M. Winter, T. Vegge, I. Cekic-Laskovic, A. Bhowmik, Digital Discovery 1, 440-447, 2022.
• Modeling the solid electrolyte interphase: Machine learning as a game changer?, D. Diddens, W.A. Appiah, Y. Mabrouk, A. Heuer, T. Vegge, A. Bhowmik, Adv. Mater. Interfaces, 2101734, 2022.
• Near infrared sensor setup for general interface detection in automatic liquid-liquid extraction processes, R. Moreno, A. Faina and K. Stoy, IEEE Sensors Journal 22, 10, 9857-9867, 2022.
• One-shot active learning for globally optimal battery electrolyte conductivity, F. Rahmanian, M. Vogler, C. Wölke, P. Yan, M. Winter, I. Cekic-Laskovic, H.S. Stein, Batt. and Supercaps, 5, e20220022, 2022.
• Perspectives on manufacturing simulations of Li-S battery cathodes, O. Arcelus and A.A. Franco, J. Phys. Energy 4, 011002, 2022.
• Phase-field investigation of lithium electrodeposition at different applied overpotentials and operating temperatures, J. Jeon, G.H. Yoon, T. Vegge, J.H. Chang, ACS Appl. Mater. Interfaces, 14, 15275-15286, 2022.
• PRISMA: A robust and intuitive tool for high-throughput processing of spectra, E. Flores, N. Mozhzhukhina, X. Li, P. Norby, A. Matic, T. Vegge, Chemistry Methods, e202100094, 2022.
• Resolving the role of configurational entropy in improving cycling performance of multicomponent hexacyanoferrate cathodes for sodium-ion batteries, Y. Ma, Y. Hu, Y. Pramudya, T. Diemant, Q. Wang, D. Goonetilleke, Y. Tang, B. Zhou, H. Hahn, W. Wenzel, M. Fichtner, Y. Ma, B. Breitung, T. Brezesinski, Adv. Funct. Mat. 32, 2202372, 2022.
• Robotic cell assembly to accelerate battery research, B. Zhang, L. Merker, A. Sanin; H.S. Stein, Digital Discovery 1, 733, 2022.

Publications 2021

• Ab initio machine learning in chemical compound space, B. Huang, O. A. von Lilienfeld, Chem. Rev. 121, 10001-10036, 2021.
• Accelerating battery characterization using neutron and synchrotron techniques: Toward a multi-modal and multi-scale standardized experimental workflow, D. Atkins, E. Capria, K. Edström, T. Famprikis, A. Grimaud, Q. Jacquet, M. Johnson, A. Matic, P. Norby, H. Reichert, J.-P. Rueff, C. Villevieille, M. Wagemaker, S. Lyonnard, Adv. Energy. Mater., 2102694, 2021.
• Artificial intelligence applied to battery research: Hype or reality?, T. Lombardo, M. Duquesnoy, H. El-Bouysidy, F. Årén, A. Gallo-Bueno, P.B. Jørgensen, A. Bhowmik, A. Demortière, E. Ayerbe, F. Alcaide, M. Reynaud, J. Carrasco, A. Grimaud, C. Zhang, T. Vegge, P. Johansson, A.A. Franco, Chem. Rev., 2021.
• Conformer-specific polar cycloaddition of dibromobutadiene with trapped propene ions, A. Kilaj, J. Wang, P. Straňák, M. Schwilk, U. Rivero, L. Xu, O.A. von Lilienfeld, J. Küpper, S. Willitsch, Nature Communications 2, 6047, 2021.
• Data management plans: The importance of data management in the BIG-MAP project, I.E. Castelli, D.J. Arismendi-Arrieta, A. Bhowmik, I. Cekic-Laskovic, S. Clark, R. Dominko, E. Flores, J. Flowers, K.U. Frederiksen, J. Friis, A. Grimaud, K. V. Hansen, L.J. Hardwick, K. Hermansson, L. Königer, H. Lauritzen, F. Le Cras, H. Li, S. Lyonnard, H. Lorrmann, N. Marzari, L. Niedzicki, G. Pizzi, F. Rahmanian, H. Stein, M. Uhrin, W. Wenzel, M. Winter, C. Wölke, T. Vegge, Batteries & Supercaps 4, 1803–1812, 2021.
• Density functional geometries and zero-point energies in ab initio thermochemical treatments of compounds with first-row atoms (H,C,N,O,F), D. Bakowies, O.A. von Lilienfeld, J. Chem. Theory Comput. 17, 4872-4890, 2021.
• Elucidating an atmospheric brown carbon species - Toward supplanting chemical intuition with exhaustive enumeration and machine learning, E. Tapavicza, G.F. von Rudorff, D.O. De Haan, M. Contin, C. George, M. Riva, and O.A. von Lilienfeld, Environ. Sci. Technol. 55, 8447-8457, 2021.
• High-throughput experimentation and computational freeway lanes for accelerated battery electrolyte and interface development research, A. Benayad, D. Diddens, A. Heuer, A.N. Krishnamoorthy, M. Maiti, F. Le Cras, M. Legallais, F. Rahmanian, Y. Shin, H. Stein, M. Winter, C. Wölke, P. Yan, I. Cekic-Laskovic, Adv. Energy. Mater., 202102678, 2021.
• Implications of the BATTERY 2030+ AI-assisted toolkit on future low-TRL battery discoveries and chemistries, A. Bhowmik, M. Berecibar, M. Casas-Cabanas, G. Csanyi, R. Dominko, K. Hermansson, M.R. Palacin, H.S. Stein, T. Vegge, Adv. Energy. Mater., 2102698, 2021.
• Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes, A. Shodiev, M. Duquesnoy, O. Arcelus, M. Chouchane, J. Lic, A.A. Franco, J. Power Sources 511, 230384, 2021.
• Machine learning based energy-free structure predictions of molecules, transition states, and solids, D. Lemm, G.F. von Rudorff, O.A. von Lilienfeld, Nat. Commun. 12, 4468, 2021.
• Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation, J. Weinreich, N.J. Browning, O.A. von Lilienfeld, J. Chem. Phys. 154, 134113, 2021.
• On-the-fly assessment of diffusion barriers of disordered transition metal oxyfluorides using local descriptors, J.H. Chang, P. B. Jørgensen, S. Loftager, A. Bhowmik, J.M. García Lastra, T. Vegge, Electrochim. Acta 388, 138551, 2021.
• Rechargeable batteries of the future -the state of the art from a BATTERY 2030+ perspective, M. Fichtner, K. Edström, E. Ayerbe, M. Berecibar, A. Bhowmik, I.E. Castelli, S. Clark, R. Dominko, M. Erakca, A.A. Franco, A. Grimaud, B. Horstmann, A. Latz, H. Lorrmann, M. Meeus, R. Narayan, F. Pammer, J. Ruhland, H. Stein, T. Vegge, M. Weil, Adv. Energy. Mater., 2102904, 2021.
• The potential of scanning electrochemical probe microscopy and scanning droplet cells in battery research, S. Daboss, F. Rahmanian, H.S. Stein, C. Kranz, Electrochem. Sci. Adv., e2100122, 2021.
• Toward a unified description of battery data, S. Clark, F.L. Bleken, S. Stier, E. Flores, C.W. Andersen, M. Marcinek, A. Szczesna-Chrzan, M. Gaberscek, M.R, Palacin, Martin Uhrin, J. Friis, Adv. Energy Mater., 2102702, 2021.
• Towards a 3D-resolved model of Si/graphite composite electrodes from manufacturing simulations, C. Liu, O. Arcelus, T. Lombardo, H. Oularbi, A.A. Franco, J. Power Sources 512, 230486, 2021.
• Towards autonomous high-throughput multiscale modelling of battery interfaces, Z. Deng, V. Kumar, F.T. Bölle, F. Caro, A.A. Franco, I.E. Castelli, P. Canepa, Z.W. Seh, Energy Environ. Sci. 2021.
• Towards better and smarter batteries by combining AI with multisensory and self-healing approaches, T. Vegge, J.‐M. Tarascon, K. Edström, Adv. Energy Mater. 11, 2100362, 2021.
• Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space, S. Heinen, G. F. von Rudorff, O.A. von Lilienfeld, J. Chem. Phys. 155, 064105, 2021.
• Training set based on uncertainty estimations for cluster expansion method, D. Kleiven, J. Akola, A. Peterson, T. Vegge, J.H. Chang, J. Phys. Energy 3, 034012, 2021.
• Understanding battery interfaces by combined characterization and simulation approaches: Challenges and perspectives, D. Atkins, E. Ayerbe, A. Benayad, F.G. Capone, E. Capria, I.E. Castelli, I. Cekic-Laskovic, R. Ciria, L. Dudy, K. Edström, M.R. Johnson, H. Li, J.M. Garcia Lastra, M.L. De Souza, V. Meunier, M. Morcrette, H. Reichert, P. Simon, J.-P. Rueff, J. Sottmann, W. Wenzel, A. Grimaud, Adv. Energy Mater. 2102687, 2021.
• Virtual computational chemistry teaching laboratories - Hands-on at a distance, R. Kobayashi, T.P.M. Goumans, N.O. Carstensen, T.M. Soini, N. Marzari, I. Timrov, S. Poncé, E.B. Linscott, C.J. Sewell, G. Pizzi, F. Ramirez, M. Bercx, S.P. Huber, C.S. Adorf, L. Talirz, J. Chem. Educ.  98, 10, 3163–3171, 2021.
• Workflow engineering in materials design within the BATTERY 2030+ project, J. Schaarschmidt, J. Yuan, T. Strunk, I. Kondov, S.P. Huber, G. Pizzi, L. Kahle, F.T. Bölle, I.E. Castelli, T. Vegge, F. Hanke, T. Hickel, J. Neugebauer, C.R.C. Rêgo, W. Wenzel, Adv. Energy Mater., 2102638, 2021.

Publications prior to start of funding period

• AI Fast Track to Battery Fast Charge, A. Bhowmik and T. Vegge, Joule 4, 710–723, 2020.
• A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning, A. Bhowmik, I. E. Castelli, J. M. Garcia-Lastra, P. B. Jørgensen,
  O. Winther, T. Vegge, Energy Storage Materials 21, 446–456, 2019.
• Inventing the Sustainable Batteries of the Future: Research Needs and Future Actions, K. Edström, Editors: R. Dominko, M. Fichtner, T. Otuszewski, S. Perraud, C. Punckt, J.-M. Tarascon, T. Vegge, M. Winter