Publications

• 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 

BIG-MAP publications

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Structural design of pyrene-functionalized TEMPO-containing polymers for enhanced electrochemical storage performance, W. Xue, H. Mutlu, H. Li, W. Wenzel, P. Theato, Polym. Chem. 12, 2643, 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.