Reference health
Machine Learning for Battery Research
105/105 checkable references clean · checked 2026-10-18
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
34 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 105 checked references that resolve
resolves10.1007/s00214-007-0310-x
The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals
resolves10.1016/j.jechem.2021.12.020
Dead lithium formation in lithium metal batteries: A phase field model
resolves10.1016/j.jpowsour.2015.09.055
Modulation of dendritic patterns during electrodeposition: A nonlinear phase-field model
resolves10.1021/acs.chemrev.9b00601
Rechargeable Alkali-Ion Battery Materials: Theory and Computation
resolves10.1021/acs.chemrev.1c00904
Applying Classical,
<i>Ab Initio</i>
, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries
resolves10.1063/1.4946894
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
resolves10.1007/s11837-014-0888-0
The Materials Genome Initiative at the National Science Foundation: A Status Report after the First Year of Funded Research
resolves10.1021/acs.chemrev.8b00728
Concepts of Artificial Intelligence for Computer-Assisted
Drug Discovery
resolves10.1109/TIE.2017.2674593
Diagnosis of Electric Vehicle Batteries Using Recurrent Neural Networks
resolves10.1016/j.jpowsour.2010.07.071
Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling
resolves10.1016/j.jpowsour.2011.10.013
A comparative study of equivalent circuit models for Li-ion batteries
resolves10.1016/j.jpowsour.2015.01.154
Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction
resolves10.1038/s41560-019-0356-8
Data-driven prediction of battery cycle life before capacity degradation
resolves10.1002/anie.202107369
Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale
resolves10.1038/s43588-022-00265-6
Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
resolves10.1021/acs.chemrev.0c00004
Big-Data Science
in Porous Materials: Materials Genomics
and Machine Learning
resolves10.1107/S0108768102003890
The Cambridge Structural Database: a quarter of a million crystal structures and rising
resolves10.1007/s11837-013-0755-4
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
resolves10.1016/j.commatsci.2012.02.002
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
resolves10.1016/j.commatsci.2008.07.016
Data mining and accelerated electronic structure theory as a tool in the search for new functional materials
resolves10.1021/acs.chemmater.0c01907
Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
resolves10.1038/s41578-020-00255-y
Emerging materials intelligence ecosystems propelled by machine learning
resolves10.1007/s10462-018-09679-z
Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey
resolves10.1016/j.compchemeng.2019.04.003
Advances and opportunities in machine learning for process data analytics
resolves10.1021/acs.jpca.7b08750
Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
resolves10.1016/j.commatsci.2012.10.028
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
resolves10.1109/TSMCC.2011.2161285
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
resolves10.1038/s41570-022-00391-9
Evaluation guidelines for machine learning tools in the chemical sciences
resolves10.1016/j.ensm.2020.06.033
Machine learning assisted materials design and discovery for rechargeable batteries
resolves10.1021/ie101513z
Using a Multilayer Perceptron Network for Thermal Conductivity Prediction of Aqueous Electrolyte Solutions
resolves10.1021/ie202681b
Estimation of Thermal Conductivity of Ionic Liquids Using a Perceptron Neural Network
resolves10.1016/j.ces.2013.07.007
A “non-linear” quantitative structure–property relationship for the prediction of electrical conductivity of ionic liquids
resolves10.1016/j.fluid.2011.10.028
Using artificial neural network to predict the ternary electrical conductivity of ionic liquid systems
resolves10.1016/j.molliq.2016.05.059
A computational intelligence scheme for estimating electrical conductivity of ternary mixtures containing ionic liquids
resolves10.1016/j.fluid.2012.04.017
Viscosity prediction of ternary mixtures containing ILs using multi-layer perceptron artificial neural network
resolves10.1016/j.fluid.2013.11.041
Estimation of viscosity of binary mixtures of ionic liquids and solvents using an artificial neural network based on the structure groups of the ionic liquid
resolves10.1016/j.molliq.2016.11.133
Estimation of viscosities of pure ionic liquids using an artificial neural network based on only structural characteristics
resolves10.1016/j.molliq.2016.07.068
On the evaluation of density of ionic liquid binary mixtures: Modeling and data assessment
resolves10.1039/C9CP03679B
Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents
resolves10.1039/C3TA13235H
An efficient rule-based screening approach for discovering fast lithium ion conductors using density functional theory and artificial neural networks
resolves10.1021/co100075f
High Throughput Methodology for Synthesis, Screening, and Optimization of Solid State Lithium Ion Electrolytes
resolves10.1016/S1452-3981(23)18428-8
Conductivity, Thermal and Neural Network Model Nanocomposite Solid Polymer Electrolyte S LiPF6)
resolves10.1002/aenm.201300060
Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms
resolves10.1039/C6EE02697D
Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
resolves10.1002/tcr.201800129
Data‐Driven Materials Exploration for Li‐Ion Conductive Ceramics by Exhaustive and Informatics‐Aided Computations
resolves10.1021/ci500752n
Informatics-Aided Density Functional Theory Study on the Li Ion Transport of Tavorite-Type LiMTO<sub>4</sub>F (M<sup>3+</sup>–T<sup>5+</sup>, M<sup>2+</sup>–T<sup>6+</sup>)
resolves10.1021/acscentsci.8b00229
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes
resolves10.1016/j.commatsci.2016.02.021
Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries
resolves10.1016/j.jmat.2017.02.002
Quantitative structure-property relationship study of cathode volume changes in lithium ion batteries using ab-initio and partial least squares analysis
resolves10.1021/acsami.9b04933
Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries
resolves10.1021/acs.jpcc.7b09760
Li(Ni,Co,Al)O<sub>2</sub> Cathode Delithiation: A Combination of Topological Analysis, Density Functional Theory, Neutron Diffraction, and Machine Learning Techniques
resolves10.1016/j.electacta.2007.08.049
Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of lithium-ion cells
resolves10.1103/PhysRevLett.114.105503
Big Data of Materials Science: Critical Role of the Descriptor
resolves10.1016/j.ensm.2021.10.029
Artificial intelligence driven in-silico discovery of novel organic lithium-ion battery cathodes
resolves10.1039/C7EE00524E
Electrolyte design strategies and research progress for room-temperature sodium-ion batteries
resolves10.1021/acs.macromol.0c01547
Design of Polymer Blend Electrolytes through a Machine Learning Approach
resolves10.1038/s41560-018-0107-2
Performance and cost of materials for lithium-based rechargeable automotive batteries
resolves10.1109/TIM.2008.2005965
Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework
resolves10.1016/S0378-7753(99)00079-8
Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology
resolves10.1016/j.ensm.2022.05.007
Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning
resolves10.1111/j.1467-9868.2011.00771.x
Regression Shrinkage and Selection via The Lasso: A Retrospective
resolves10.1016/j.joule.2021.10.010
Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols
resolves10.1038/s41586-020-1994-5
Closed-loop optimization of fast-charging protocols for batteries with machine learning
resolves10.3390/electronics8080832
Machine Learning Interpretability: A Survey on Methods and Metrics
resolves10.1039/D0NJ02592E
Interpretable machine learning as a tool for scientific discovery in chemistry
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