Artificial Intelligence Investigation of NMC Cathode Manufacturing Parametersinterdependencies

[1]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

[2]  Christoph Herrmann,et al.  Data mining in battery production chains towards multi-criterial quality prediction , 2019, CIRP Annals.

[3]  Ali Emadi,et al.  State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach , 2018, Journal of Power Sources.

[4]  Kristen A. Severson,et al.  Data-driven prediction of battery cycle life before capacity degradation , 2019, Nature Energy.

[5]  Abhinav Vishnu,et al.  Deep learning for computational chemistry , 2017, J. Comput. Chem..

[6]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for photovoltaic applications: A review , 2008 .

[7]  M. Winter,et al.  Before Li Ion Batteries. , 2018, Chemical reviews.

[8]  O. Winther,et al.  A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning , 2019, Energy Storage Materials.

[9]  Alejandro A. Franco,et al.  Tracking variabilities in the simulation of Lithium Ion Battery electrode fabrication and its impact on electrochemical performance , 2019, Electrochimica Acta.

[10]  D. Brandell,et al.  Boosting Rechargeable Batteries R&D by Multiscale Modeling: Myth or Reality? , 2019, Chemical reviews.

[11]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for sizing photovoltaic systems: A review , 2009 .

[12]  D. Sokolov,et al.  Ferromagnetic ordering along the hard axis in the Kondo lattice YbIr3Ge7 , 2019, Physical Review B.

[13]  Gunther Reinhart,et al.  Data mining in lithium-ion battery cell production , 2019, Journal of Power Sources.

[14]  Pascal Vincent,et al.  GSNs : Generative Stochastic Networks , 2015, ArXiv.

[15]  Jianzhou Wang,et al.  Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system , 2019, Renewable Energy.

[16]  Claus Daniel,et al.  Prospects for reducing the processing cost of lithium ion batteries , 2015 .

[17]  Garima Shukla,et al.  Multiscale Simulation Platform Linking Lithium Ion Battery Electrode Fabrication Process with Performance at the Cell Level. , 2017, The journal of physical chemistry letters.

[18]  Ulrike Krewer,et al.  Simulating Process-Product Interdependencies in Battery Production Systems , 2018 .

[19]  R. Batra,et al.  Physically informed artificial neural networks for atomistic modeling of materials , 2018, Nature Communications.

[20]  Yogesh Kumar Dwivedi,et al.  Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda , 2019, Int. J. Inf. Manag..

[21]  Alán Aspuru-Guzik,et al.  Neural Networks for the Prediction of Organic Chemistry Reactions , 2016, ACS central science.

[22]  Christoph Herrmann,et al.  Toward Data‐Driven Applications in Lithium‐Ion Battery Cell Manufacturing , 2020, Energy Technology.

[23]  J. Tarascon,et al.  Towards greener and more sustainable batteries for electrical energy storage. , 2015, Nature chemistry.