Exploring the Possibility of Machine Learning for Predicting Ionic Conductivity of Solid-State Electrolytes

Unlike conventional liquid electrolytes, solid-state electrolytes (SSEs) have gained increased attention in the domain of all-solid-state lithium-ion batteries (ASSBs) due to their safety features, higher energy/power density, better electrochemical stability, and a broader electrochemical window. SSEs, however, face several difficulties, such as poorer ionic conductivity, complicated interfaces, and unstable physical characteristics. Vast research is still needed to find compatible and appropriate SSEs with improved properties for ASSBs. Traditional trial-and-error procedures to find novel and sophisticated SSEs require vast resources and time. Machine learning (ML), which has emerged as an effective and trustworthy tool for screening new functional materials, was recently used to forecast new SSEs for ASSBs. In this study, we developed an ML-based architecture to predict ionic conductivity by utilizing the characteristics of activation energy, operating temperature, lattice parameters, and unit cell volume of various SSEs. Additionally, the feature set can identify distinct patterns in the data set that can be verified using a correlation map. Because they are more reliable, the ensemble-based predictor models can more precisely forecast ionic conductivity. The prediction can be strengthened even further, and the overfitting issue can be resolved by stacking numerous ensemble models. The data set was split into 70:30 ratios to train and test with eight predictor models. The maximum mean-squared error and mean absolute error in training and testing for the random forest regressor (RFR) model were obtained as 0.001 and 0.003, respectively.

[1]  Zhilong Wang,et al.  Rapid discovery of inorganic-organic solid composite electrolytes by unsupervised learning , 2022, Chemical Engineering Journal.

[2]  Tong-Yi Zhang,et al.  Data-driven glass-forming ability criterion for bulk amorphous metals with data augmentation , 2022, Journal of Materials Science & Technology.

[3]  T. Krilavičius,et al.  A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem , 2022, Mathematics.

[4]  Rui Zhang,et al.  The chemical origin of temperature-dependent lithium-ion concerted diffusion in sulfide solid electrolyte Li10GeP2S12 , 2022, Journal of Energy Chemistry.

[5]  George Azzopardi,et al.  A framework for feature selection through boosting , 2022, Expert Syst. Appl..

[6]  A. Sumboja,et al.  Improving ionic conductivity of doped Li7La3Zr2O12 using optimized machine learning with simplistic descriptors , 2022, Materials Letters.

[7]  T. Kozawa,et al.  Low temperature synthesis of Ga-doped Li7 La3 Zr2 O12 garnet-type solid electrolyte by mechanical method , 2021, Advanced Powder Technology.

[8]  Harsh A. Chaliyawala,et al.  Review—Inorganic Solid State Electrolytes: Insights on Current and Future Scope , 2021, Journal of The Electrochemical Society.

[9]  Nongnuch Artrith,et al.  Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning , 2021, Frontiers in Energy Research.

[10]  Alex Rodriguez,et al.  Unsupervised Learning Methods for Molecular Simulation Data , 2021, Chemical reviews.

[11]  Okke Batelaan,et al.  Ensemble machine learning paradigms in hydrology: A review , 2021, Journal of Hydrology.

[12]  Darren H. S. Tan,et al.  Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning , 2021, ACS Energy Letters.

[13]  Steve W. Martin,et al.  Challenges for and Pathways toward Li-Metal-Based All-Solid-State Batteries , 2021, ACS Energy Letters.

[14]  James Marcicki,et al.  Opportunities and Challenges of Lithium Ion Batteries in Automotive Applications , 2021 .

[15]  A. Abdulazeez,et al.  CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW , 2021, Iraqi Journal for Computers and Informatics.

[16]  Jennifer L. M. Rupp,et al.  Text mining for processing conditions of solid-state battery electrolytes , 2020 .

[17]  Alejandro A. Franco,et al.  Data-driven assessment of electrode calendering process by combining experimental results, in silico mesostructures generation and machine learning , 2020, Journal of Power Sources.

[18]  Yang‐Kook Sun.  Promising All-Solid-State Batteries for Future Electric Vehicles , 2020, ACS Energy Letters.

[19]  Yue Liu,et al.  Machine learning assisted materials design and discovery for rechargeable batteries , 2020, Energy Storage Materials.

[20]  Wolfgang G. Zeier,et al.  Between Liquid and All Solid: A Prospect on Electrolyte Future in Lithium‐Ion Batteries for Electric Vehicles , 2020 .

[21]  Chin-Hui Lee,et al.  On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression , 2020, IEEE Signal Processing Letters.

[22]  S. Adams,et al.  A Database of Ionic Transport Characteristics for Over 29 000 Inorganic Compounds , 2020, Advanced Functional Materials.

[23]  Eric D. Kolaczyk,et al.  Statistical Analysis of Network Data with R , 2020, Use R!.

[24]  K. Hippalgaonkar,et al.  Machine learning-assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors using facile descriptors , 2020, Journal of Physics Communications.

[25]  A. Manthiram.  A reflection on lithium-ion battery cathode chemistry , 2020, Nature Communications.

[26]  K. Tsuda,et al.  Optimization of a Heterogeneous Ternary Li3PO4–Li3BO3–Li2SO4 Mixture for Li-Ion Conductivity by Machine Learning , 2019, The Journal of Physical Chemistry C.

[27]  Annamária R. Várkonyi-Kóczy,et al.  Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods , 2019, Lecture Notes in Networks and Systems.

[28]  J. Kleesiek,et al.  Wie funktioniert maschinelles Lernen? , 2019, Der Radiologe.

[29]  Rickmer Braren,et al.  [A primer on machine learning]. , 2019, Der Radiologe.

[30]  M. Kuenzel,et al.  Elucidating the Effect of Iron Doping on the Electrochemical Performance of Cobalt‐Free Lithium‐Rich Layered Cathode Materials , 2019, Advanced Energy Materials.

[31]  Hongli Zhu,et al.  Sulfide‐Based Solid‐State Electrolytes: Synthesis, Stability, and Potential for All‐Solid‐State Batteries , 2019, Advanced materials.

[32]  Ekin D Cubuk,et al.  Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data. , 2019, The Journal of chemical physics.

[33]  Elsa Olivetti,et al.  A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction , 2019, ACS central science.

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

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

[36]  Brandon M. Greenwell,et al.  Interpretable Machine Learning , 2019, Hands-On Machine Learning with R.

[37]  Dhiya Al-Jumeily,et al.  A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science , 2019, Unsupervised and Semi-Supervised Learning.

[38]  Chen‐Zi Zhao,et al.  Recent Advances in Energy Chemical Engineering of Next-Generation Lithium Batteries , 2018, Engineering.

[39]  Gowoon Cheon,et al.  Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials , 2018, Chemistry of Materials.

[40]  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.

[41]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[42]  Stefanie Jegelka,et al.  Virtual screening of inorganic materials synthesis parameters with deep learning , 2017, npj Computational Materials.

[43]  Arun Mannodi-Kanakkithodi,et al.  Mining materials design rules from data: The example of polymer dielectrics , 2017 .

[44]  A. Mauger,et al.  Critical review on lithium-ion batteries: are they safe? Sustainable? , 2017, Ionics.

[45]  Chandrasegar Thirumalai,et al.  Decision making system using machine learning and Pearson for heart attack , 2017, 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA).

[46]  Zachary W. Ulissi,et al.  To address surface reaction network complexity using scaling relations machine learning and DFT calculations , 2017, Nature Communications.

[47]  Ekin D. Cubuk,et al.  Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials , 2017 .

[48]  Wei Chen,et al.  A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds , 2016, Scientific Reports.

[49]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[50]  Surya R. Kalidindi,et al.  Materials Data Science: Current Status and Future Outlook , 2015 .

[51]  Klaus Nordhausen,et al.  Statistical Analysis of Network Data with R , 2015 .

[52]  Zhenwei Li,et al.  Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces. , 2015, Physical review letters.

[53]  Ashok Kumar Baral,et al.  Fast Solid-State Li Ion Conducting Garnet-Type Structure Metal Oxides for Energy Storage. , 2015, The journal of physical chemistry letters.

[54]  Kazunori Takada,et al.  A sulphide lithium super ion conductor is superior to liquid ion conductors for use in rechargeable batteries , 2014 .

[55]  Ji‐Guang Zhang,et al.  Lithium metal anodes for rechargeable batteries , 2014 .

[56]  Kristof T. Schütt,et al.  How to represent crystal structures for machine learning: Towards fast prediction of electronic properties , 2013, 1307.1266.

[57]  Daniel R. Goethel,et al.  Simulation Modeling as a Tool for Synthesis of Stock Identification Information , 2014 .

[58]  Muratahan Aykol,et al.  Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) , 2013 .

[59]  Kristin A. Persson,et al.  Commentary: The Materials Project: A materials genome approach to accelerating materials innovation , 2013 .

[60]  Svetlozar Nestorov,et al.  The Computational Materials Repository , 2012, Computing in Science & Engineering.

[61]  J. Sakamoto,et al.  Room temperature elastic moduli and Vickers hardness of hot-pressed LLZO cubic garnet , 2012, Journal of Materials Science.

[62]  Marco Buongiorno Nardelli,et al.  AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations , 2012 .

[63]  E. Newport,et al.  Science Current Directions in Psychological Statistical Learning : from Acquiring Specific Items to Forming General Rules on Behalf Of: Association for Psychological Science , 2022 .

[64]  Masayuki Nogami,et al.  Multivariate Method-Assisted Ab Initio Study of Olivine-Type LiMXO4 (Main Group M2+–X5+ and M3+–X4+) Compositions as Potential Solid Electrolytes , 2012 .

[65]  Peter Moeck,et al.  Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration , 2011, Nucleic Acids Res..

[66]  Anubhav Jain,et al.  Evaluation of Tavorite-Structured Cathode Materials for Lithium-Ion Batteries Using High-Throughput Computing. , 2011 .

[67]  Anubhav Jain,et al.  Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations , 2011 .

[68]  Anubhav Jain,et al.  Evaluation of Tavorite-Structured Cathode Materials for Lithium-Ion Batteries Using High-Throughput Computing , 2011 .

[69]  William Stafford Noble,et al.  Support vector machine , 2013 .

[70]  Jiming Jiang,et al.  Mean squared error of empirical predictor , 2004, math/0406455.

[71]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[72]  Benzion Boukai,et al.  On the mean squared error, the mean absolute error and the like , 1999 .

[73]  I. D. Brown,et al.  The inorganic crystal structure data base , 1983, J. Chem. Inf. Comput. Sci..

[74]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..