Publications

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)

• Autonomous millimeter scale high throughput battery research system, F. Rahmanian, S. Fuchs, B. Zhang, M. Fichtner, H.S. Stein, Digital Discovery, 2024.
  
  
• Enhancing ReaxFF for molecular dynamics simulations of lithium-ion batteries: an interactive reparameterization protocol, P. De Angelis, R. Cappabianca, M. Fasano, P. Asinari, E. Chiavazzo, Scientific Reports 14, 978, 2024. - Modeling of SEI formation and growth. (WP3)

• Exploring reproducible nonaqueous scanning droplet cell electrochemistry in model battery chemistries, A. Sanin, H.S. Stein, Chem. Mater.,  2024.

• Jahn-Teller distortions and phase transitions in LiNiO₂: Insights from ab initio molecular dynamics and variable-temperature X-ray diffraction, A.R. Genreith-Schriever, A. Alexiu, G.S. Phillips, C.S. Coates, L.A.V. Nagle-Cocco, J.D. Bocarsly, F.N. Sayed, S.E. Dutton, C.P. Grey, Chem. Mater., 2024. - This work combines AIMD simulations of LiNiO₂ with careful analysis of diffraction data of LiNiO2 to understand the Jahn Teller (JT) distortion in this material. The role of anti-site Li/Ni mixing in preventing cooperative JT distortions is highlighted. (WP5)

• Greener, safer and better performing aqueous binder for positive electrode manufacturing of sodium ion batteries, R. Xu, V. Pamidi, Y. Tang, S. Fuchs, H.S. Stein, B. Dasari, Z. Zhao-Karger, S. Behara, Y. Hu, S. Trivedi, M.A. Reddy, P. Barpanda, M. Fichtner, ChemSusChem, 2024.

• Strain and collapse during lithiation of layered transition metal oxides: a unified picture, T. Jousseaume, J.-F. Colin, M. Chandesris, S. Lyonnard, S. Tardif, Energy Environ. Sci. 2024. -The article highlights the similar structural evolution of NMC and LNO and establishes a critical lithium concentration below which strain and lattice distortion develop exponentially. (KD4, WP5)

• Surface analysis insight note: Accounting for X-ray beam damage effects in positive electrode-electrolyte interphase investigations, R. Fantin, A. Van Roekeghem, J.-P. Rueff, A. Benayad, Surf. Interface Anal., 2024. - This paper highlight the effect of X-ray beam damage on composite LiNiO₂ electrode containing PVDF in the case of laboratory and synchrotron based XPS and HAXPES. The work describes the requirement needed to avoid such effect especially on the PVDF. (WP5)

• 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.

• A solution-mediated pathway for the growth of the solid electrolyte interphase in lithium-ion batteries, M. Esmaeilpour, S. Jana, H. Li, M. Soleymanibrojeni, W. Wenzel, Adv. Energy Mater. 13, 2203966, 2023. - A multiscale approach to comprehensively characterize the growth and composition of the SEI on the basis of a chemistry-specific reaction network. (WP3)

• Accelerated workflow for antiperovskite‐based solid state electrolytes, B.H. Sjølin, P.B. Jørgensen, A. Fedrigucci, T. Vegge, A. Bhowmik, I.E. Castelli, Batteries and Supercaps 6, e202300041, 2023.

• An active learning approach to model solid-electrolyte interphase formation in Li-ion batteries, M. Soleymanibrojeni, C. R. Caldeira Rego, M. Esmaeilpour, W. Wenzel, J. Mater. Chem. A, 2024. - Multiscale modeling of SEI formation and growth. (WP3)

• An overview on transport phenomena within solid electrolyte interphase and their impact on the performance and durability of lithium-ion batteries, R. Cappabianca, P. De Angelis, M. Fasano, E. Chiavazzo, P. Asinari, Energies 16, 13, 5003, 2023. - Modeling of SEI formation and growth. (WP3)

• 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)

• Brokering between tenants for an international materials acceleration platform, M. Vogler, J. Busk, H. Hajiyani, P.B. Jørgensen, N. Safaei, I.E. Castelli, F.F. Ramirez, J. Carlsson, G. Pizzi, S. Clark, F. Hanke, A. Bhowmik, H.S. Stein, Matter 6 (9) 2647-2665, 2023. - Integrating accelerated labs and instruments to build the first international MAP. (WP6, 8, 9, 10, 11)
  
  
• 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. (WP6, WP10, WP9)

• 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)

• Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes, A. Szczęsna-Chrzan, M. Vogler, P. Yan, G.Z. Żukowska, C. Wölke, A. Ostrowska, S. Szymańska, M. Marcinek, M. Winter, I. Cekic-Laskovic, W. Wieczorek, H.S. Stein, J. Mat. Chem. A 11, 13483-13492, 2023

• Leveraging composition-based material descriptors for machine learning optimization, G. Trezza, E. Chiavazzo, Mater. Today Commun. 36, 106579, 2023.

• Lithium ion battery electrode manufacturing model accounting for 3D realistic shapes of active material particles, J. Xu, A.C. Ngandjong, C. Liu, F.M. Zanotto, O. Arcelus, A. Demortière, A.A. Franco, J. Power Sources, 554, 232294, 2023.

• Li₅NCl₂: A fully-reduced, highly-disordered nitride-halide electrolyte for solid-state batteries with lithium-metal anodes, V. Landgraf, T. Famprikis, J. de Leeuw, L.J. Bannenberg, S. Ganapathy, M. Wagemaker, ACS Appl. Energy Mater. 6, 1661–1672, 2023.

• Machine learning for optimal electrode wettability in lithium ion batteries. A.E. Malki, M. Asch, O. Arcelus, A. Shodiev, J. Yu, A.A. Franco, J. Power Sources Adv. 20, 100114, 2023.
  
  Machine learning force fields for molecular liquids: Ethylene carbonate/ethyl methyl carbonate binary solvent, I.-B. Magdău, D.J. Arismendi-Arrieta, H.E. Smith, C.P. Grey, K. Hermansson, G Csányi, npj Computational Materials 9, 146, 2023. - Machine learning potentials, ab initio benchmarking, molecular dynamics, binary carbonate solvents.

• Materials Funnel 2.0 - Data-driven hierarchical search for exploration of vast chemical spaces, R.O. Ochoa, B. Benediktsson, R. Sechi, P.B. Jørgensen, A. Bhowmik, J. Mater. Chem. A 11, 26551-26561, 2023. Green open access.
  

• Mechanistic understanding of the correlation between structure and dynamics of liquid carbonate electrolytes: Impact of polarization, M. Maiti, A.N. Krishnamoorthy, Y. Mabrouk, N. Mozhzhukhina, A. Matic, D. Diddens, A. Heuer, Phys. Chem. Chem. Phys. 2023. (KD8, WP3, WP5, WP6)

• MultiXC-QM9: Large dataset of molecular and reaction energies from multi-level quantum chemical methods, S. Nandi, T. Vegge, A. Bhowmik, Sci. Data 10, 783, 2023.

• Neural network ansatz for periodic wave functions and the homogeneous electron gas, M. Wilson, S. Moroni, M. Holzmann, N. Gao, F. Wudarski, T. Vegge, A. Bhowmik, Phys. Rev. B 107, 235139, 2023. Green open access. (WP2)

• OSSCAR, an open platform for collaborative development of computational tools for education in science, D. Du, T. J. Baird, S. Bonella, G. Pizzi, Comput. Phys. Commun. 282, 108546, 2023. (KD8, WP9)

• Phase separating electrode materials - chemical inductors? K. Zelič, I. Mele. A. Bhowmik, T. Katrašnik, Energy Storage Mater. 56, 489-494, 2023. (WP3)

• Sensitivity analysis methodology for battery degradation models, W. A. Appiah, J. Busk, T. Vegge, A. Bhowmik, Electrochim. Acta 439, 141430, 2023. (KD2, KD11, WP3, WP11)
  
  
• The effect of doping process route on LiNiO₂ cathode material properties, S.L. Dreyer, P. Kurzhals, S.B. Seiffert, P. Müller, A. Kondrakov, T. Brezesinski, J. Janek,  J. Electrochem. Soc. 170, 060530, 2023. - Highlights the impact of the process (co-calcination, co-precipitation, impregnation) of introducing the dopant Zr into the model cathode active material LiNiO2 on its physicochemical and electrochemical properties. (WP6)
  
  
• Time and space resolved operando synchrotron X-ray and neutron diffraction study of Nmc811/Si-Gr 5 Ah pouch cells, K.V. Graae, X. Li, D.R. Sørensen, E. Ayerbe, I. Boyano, D. Sheptyakov, M.R.V. Jørgensen, P. Norby, J. Power Sources 570, 232993, 2023.

• Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows, M. Bonacci, J. Qiao, N. Spallanzani, A. Marrazzo, G. Pizzi, E. Molinari, D. Varsano, A. Ferretti, D. Prezzi, npj Comput. Mater. 9, 74, 2023. - The manuscript presents algorithms and AiiDA-based workflows to fully automate many-body perturbation theory calculations, enabling high-throughput computational screening based on accurate excited-state properties of materials. (WP2, WP9)
  
  
• Toward operando characterization of interphases in batteries, J. Maibach, J. Rizell, A. Matic, N. Mozhzhukhina, ACS Materials Lett. 5, 9, 2431-2444, 2023. - Perspective on the surface sensitive techniques suitable for the operando electrode-electrolyte interphase characterisation. (WP5)
  
  
• 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. (WP11)
  
  
• Understanding the patterns that neural networks learn from chemical spectra, L. H. Rieger, M. Wilson, T. Vegge, E. Flores, Digital discovery 2, 1957-1968, 2023. - We show a simple, shallow neural network model classifies the functional groups of small organic compounds from their infrared (IR) spectra. The network learns the characteristic IR group frequencies of the functional groups, using peaks, shoulders, doublets and silent regions in the spectra to make predictions. It provides BIG-MAP with a robust tool for autonomous and explainable spectral identification. (WP5, WP11)

• wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows, E. Gelžinytė, S. Wengert, T.K. Stenczel, H.H. Heenen, K. Reuter, G. Csányi, N. Bernstein, J. Chem. Phys. 159, 124801, 2023.

Publications 2022

• Accelerating the adoption of research data management strategies, J. Medina, A.W. Ziaullah, H. Park, I.E. Castelli, A. Shaon, H. Bensmail, F. El-Mellouhi, Matter 5, 3614-3642, 2022.
• Advances in studying interfacial reactions in rechargeable batteries by photoelectron spectroscopy, I. Källquist, R. Le Ruyet, H. Liu; R. Mogensen, M.-T. Lee, K. Edström, A.J. Naylor, J. Mater. Chem. A 10, 19466-19505, 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.
• Cheap turns superior: A linear regression-based correction method to reaction energy from the DFT, S. Nandi, J. Busk, P.B. Jørgensen, T. Vegge, A. Bhowmik, J. Chem. Inf. Model. 62, 4727-4735, 2022. Green open access.
• 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.
• Deconvoluting the benefits of porosity distribution in layered electrodes on the electrochemical performance of Li-ion batteries, A. Shodiev, M. Chouchane, M. Gaberscek, O. Arcelus, J. Xu, H. Oularbi, J. Yu, J. Li, M. Morcrette, A.A. Franco, Energy Storage Materials 47, 462-471, 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.
• Design of workflows for crosstalk detection and lifetime deviation onset in Li-ion batteries, L. Ward, S. Babinec, E.J. Dufek, D.A. Howey, V. Viswanathan, M. Aykol, D.A.C. Beck, B. Blaiszik, B.-R, Chen, G. Crabtree, S. Clark, V. De Angelis, P. Dechent, M. Dubarry, E.E. Eggleton, D.P. Finegan, I. Foster, C.B. Gopal, P.K. Herring, V.W. Hu, N.H. Paulson, Y. Preger, D. Uwe-Sauer, K. Smith, S.W. Snyder, S. Sripad, T.R. Tanim, L. Teo, Joule 6 (10), P2253-2271, 2022. Green open access. - In this article, we define the electrochemical protocols necessary to describe chemical and/or physical events at the origin for “knees” in capacity lifetime.
  
  
• 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.
• Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics, M. Duquesnoy, T. Lombardo, F. Caro, F. Haudiquez, A.C. Ngandjong, J. Xu, H. Oularbi, A.A. Franco, npj Comput. Mater. 8, 161, 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.

• NeuralNEB - neural networks can find reaction paths fast, M. Schreiner, A. Bhowmik, T. Vegge, P.B. Jørgensen, O. Winther, Mach. Learn.: Sci. Technol. 3, 045022, 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.
• Principles of the battery data genome, L. Ward, S. Babinec, E.J. Dufek, D.A. Howey, V. Viswanathan, M. Aykol, D.A.C. Beck, B. Blaiszik, B.-R. Chen, G. Crabtree, S. Clark, V. De Angelis, P. Dechent, M. Dubarry, E.E. Eggleton, D.P. Finegan, I. Foster, C.B. Gopal, P.K. Herring, V.W. Hu, N.H. Paulson, Y. Preger, D. Uwe-Sauer, K. Smith, S.W. Snyder, S. Sripad, T.R. Tanim, L. Teo, Joule 6, 2253-2271, 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.
• Selected machine learning of HOMO–LUMO gaps with improved data-efficiency, B. Mazouin, A.A. Schöpfer, O.A. von Lilienfeld, Mater. Adv. 3, 8306, 2022.
• Transition1x - a dataset for building generalizable reactive machine learning potentials. M. Schreiner, A. Bhowmik, T. Vegge, J. Busk, O. Winther. Scientific Data 9, 779, 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.
• Digitalization of battery manufacturing: Current status, challenges, and opportunities, E. Ayerbe, M. Berecibar, S. Clark, A.A. Franco, J. Ruhland, Adv. Energy Mater. 12, 2102696, 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 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 sets 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