On the public citation graph

Bandgap prediction of metal halide perovskites using regression machine learning models

https://doi.org/10.1016/j.physleta.2021.127800
0 signed · 121 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/adma.201403140openalex-snapshot
Planar CH 3 NH 3 PbBr 3 Hybrid Solar Cells with 10.4% Power Conversion Efficiency, Fabricated by Controlled Crystallization in the Spin‐Coating Process
inferredcites →https://doi.org/10.1002/adma.201605881openalex-snapshot
Hybrid Lead Halide Perovskites for Ultrasensitive Photoactive Switching in Terahertz Metamaterial Devices
inferredcites →https://doi.org/10.1002/adma.201907864openalex-snapshot
Heavy Water Additive in Formamidinium: A Novel Approach to Enhance Perovskite Solar Cell Efficiency
inferredcites →https://doi.org/10.1002/adts.201900178openalex-snapshot
Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics
inferredcites →https://doi.org/10.1002/aenm.201400915openalex-snapshot
New Light‐Harvesting Materials Using Accurate and Efficient Bandgap Calculations
inferredcites →https://doi.org/10.1002/anie.201506588openalex-snapshot
Corrigendum: Pseudohalide‐Induced Moisture‐Tolerance in Perovskite CH3NH3Pb(SCN)2I Thin Films
inferredcites →https://doi.org/10.1002/ente.201901449openalex-snapshot
Assessment of Reproducibility, Hysteresis, and Stability Relations in Perovskite Solar Cells Using Machine Learning
inferredcites →https://doi.org/10.1002/ente.202000901openalex-snapshot
Toward Real Setting Applications of Organic and Perovskite Solar Cells: A Comparative Review
inferredcites →https://doi.org/10.1002/ente.202000912openalex-snapshot
Stability of Organic and Hybrid Perovskite Solar Cells
inferredcites →https://doi.org/10.1007/978-0-387-77708-5openalex-snapshot
Perovskite Oxide for Solid Oxide Fuel Cells
inferredcites →https://doi.org/10.1007/springerreference_72389openalex-snapshot
Isotonic Regression Problems
inferredcites →https://doi.org/10.1016/b978-0-12-751541-0.x5021-4openalex-snapshot
Applying Contemporary Statistical Techniques
inferredcites →https://doi.org/10.1016/j.commatsci.2018.04.033openalex-snapshot
Predicting the thermodynamic stability of perovskite oxides using machine learning models
inferredcites →https://doi.org/10.1016/j.commatsci.2020.109858openalex-snapshot
Importance of structural deformation features in the prediction of hybrid perovskite bandgaps
inferredcites →https://doi.org/10.1016/j.commatsci.2021.110528openalex-snapshot
Rapid discovery of narrow bandgap oxide double perovskites using machine learning
inferredcites →https://doi.org/10.1016/j.commatsci.2021.110530openalex-snapshot
Graph representational learning for bandgap prediction in varied perovskite crystals
inferredcites →https://doi.org/10.1016/j.conbuildmat.2019.02.136openalex-snapshot
Prediction and validation of alternative fillers used in micro surfacing mix-design using machine learning techniques
inferredcites →https://doi.org/10.1016/j.dyepig.2006.08.029openalex-snapshot
The vast colour spectrum of ternary metal oxynitride pigments
inferredcites →https://doi.org/10.1016/j.jallcom.2020.158210openalex-snapshot
Cation reorientation and octahedral tilting in the metal-organic perovskites MAPI and FAPI
inferredcites →https://doi.org/10.1016/j.jsv.2017.03.025openalex-snapshot
Sound quality prediction based on systematic metric selection and shrinkage: Comparison of stepwise, lasso, and elastic-net algorithms and clustering preprocessing
inferredcites →https://doi.org/10.1016/j.nanoen.2019.01.060openalex-snapshot
Cation influence on carrier dynamics in perovskite solar cells
inferredcites →https://doi.org/10.1016/j.nanoen.2020.105546openalex-snapshot
Critical review of machine learning applications in perovskite solar research
inferredcites →https://doi.org/10.1017/cbo9780511626166.003openalex-snapshot
Exploratory data analysis
inferredcites →https://doi.org/10.1021/acs.chemmater.7b04516openalex-snapshot
Anharmonicity and Octahedral Tilting in Hybrid Vacancy-Ordered Double Perovskites
inferredcites →https://doi.org/10.1021/acs.jpcc.5b07891openalex-snapshot
GW Band Structures and Carrier Effective Masses of CH3NH3PbI3 and Hypothetical Perovskites of the Type APbI3: A = NH4, PH4, AsH4, and SbH4
inferredcites →https://doi.org/10.1021/acs.jpclett.1c02105openalex-snapshot
Advances in Flexible Memristors with Hybrid Perovskites
inferredcites →https://doi.org/10.1021/acs.jpclett.9b03181openalex-snapshot
Interfacial Mechanism for Efficient Resistive Switching in Ruddlesden–Popper Perovskites for Non-volatile Memories
inferredcites →https://doi.org/10.1021/acsami.9b00210openalex-snapshot
Enhanced Stability and Band Gap Tuning of α-[HC(NH 2 ) 2 ]PbI 3 Hybrid Perovskite by Large Cation Integration
inferredcites →https://doi.org/10.1021/acsami.9b00793openalex-snapshot
Role of Water in Suppressing Recombination Pathways in CH 3 NH 3 PbI 3 Perovskite Solar Cells
inferredcites →https://doi.org/10.1021/ic050244qopenalex-snapshot
Tuning the Band Gap in Hybrid Tin Iodide Perovskite Semiconductors Using Structural Templating
inferredcites →https://doi.org/10.1021/ja809598ropenalex-snapshot
Organometal Halide Perovskites as Visible-Light Sensitizers for Photovoltaic Cells
inferredcites →https://doi.org/10.1021/jp411112kopenalex-snapshot
Formamidinium-Containing Metal-Halide: An Alternative Material for Near-IR Absorption Perovskite Solar Cells
inferredcites →https://doi.org/10.1021/nl500390fopenalex-snapshot
Atomistic Origins of High-Performance in Hybrid Halide Perovskite Solar Cells
inferredcites →https://doi.org/10.1038/nphoton.2014.82openalex-snapshot
Lead-free solid-state organic–inorganic halide perovskite solar cells
inferredcites →https://doi.org/10.1038/s41524-019-0177-0openalex-snapshot
Identifying Pb-free perovskites for solar cells by machine learning
inferredcites →https://doi.org/10.1038/s41524-020-0307-8openalex-snapshot
Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural network
inferredcites →https://doi.org/10.1038/s41563-020-0784-7openalex-snapshot
Metal halide perovskites for light-emitting diodes
inferredcites →https://doi.org/10.1038/srep00591openalex-snapshot
Lead Iodide Perovskite Sensitized All-Solid-State Submicron Thin Film Mesoscopic Solar Cell with Efficiency Exceeding 9%
inferredcites →https://doi.org/10.1038/srep19375openalex-snapshot
Machine learning bandgaps of double perovskites
inferredcites →https://doi.org/10.1039/c1ee02717dopenalex-snapshot
Computational screening of perovskite metal oxides for optimal solar light capture
inferredcites →https://doi.org/10.1039/c4sc02211dopenalex-snapshot
Solid-state principles applied to organic–inorganic perovskites: new tricks for an old dog
inferredcites →https://doi.org/10.1039/c4ta05284fopenalex-snapshot
Assessment of polyanion (BF 4 − and PF 6 − ) substitutions in hybrid halide perovskites
inferredcites →https://doi.org/10.1039/c8cp06528dopenalex-snapshot
Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning
inferredcites →https://doi.org/10.1039/c9tc06632bopenalex-snapshot
A progressive learning method for predicting the band gap of ABO 3 perovskites using an instrumental variable
inferredcites →https://doi.org/10.1063/1.4893495openalex-snapshot
Bandgap calculations and trends of organometal halide perovskites
inferredcites →https://doi.org/10.1088/0953-8984/22/25/253202openalex-snapshot
Electronic structure calculations with GPAW: a real-space implementation of the projector augmented-wave method
inferredcites →https://doi.org/10.1088/1361-648x/aa680eopenalex-snapshot
The atomic simulation environment—a Python library for working with atoms
inferredcites →https://doi.org/10.1103/physrevb.82.115106openalex-snapshot
Kohn-Sham potential with discontinuity for band gap materials
inferredcites →https://doi.org/10.1103/physrevb.89.205118openalex-snapshot
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
inferredcites →https://doi.org/10.1103/physrevmaterials.2.025401openalex-snapshot
Octahedral tilting instabilities in inorganic halide perovskites
inferredcites →https://doi.org/10.1103/physrevmaterials.2.043802openalex-snapshot
Predictions of new AB O 3 perovskite compounds by combining machine learning and density functional theory
inferredcites →https://doi.org/10.1103/physrevmaterials.2.085407openalex-snapshot
Machine learning the band gap properties of kesterite I2−II−IV−V4 quaternary compounds for photovoltaics applications
inferredcites →https://doi.org/10.1109/mci.2021.3084435openalex-snapshot
A Self-Adaptive Mutation Neural Architecture Search Algorithm Based on Blocks
inferredcites →https://doi.org/10.1126/sciadv.aax8821openalex-snapshot
Excitons in 2D perovskites for ultrafast terahertz photonic devices
inferredcites →https://doi.org/10.1126/science.1228604openalex-snapshot
Efficient Hybrid Solar Cells Based on Meso-Superstructured Organometal Halide Perovskites
inferredcites →https://doi.org/10.1126/science.aad5845openalex-snapshot
A mixed-cation lead mixed-halide perovskite absorber for tandem solar cells
inferredcites →https://doi.org/10.1142/s0129065721500350openalex-snapshot
A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks
inferredcites →https://doi.org/10.1198/tech.2004.s233openalex-snapshot
Applying Contemporary Statistical Techniques
inferredcites →https://doi.org/10.18637/jss.v032.i05openalex-snapshot
Isotone Optimization in R : Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods
inferredcites →https://doi.org/10.2307/2529486openalex-snapshot
Exploratory Data Analysis
inferredcites →https://doi.org/10.48550/arxiv.1803.06042openalex-snapshot
Stability Engineering of Halide Perovskite via Machine Learning
inferredcites →https://doi.org/10.56042/ijems.v27i4.44862openalex-snapshot
Diagnosis of bearing faults using multi fusion signal processing techniques and mutual information
inferredcites →https://doi.org/10.5937/fmet1904865popenalex-snapshot
Texture classification of machined surfaces using image processing and machine learning techniques

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4588485openalex-extracted
System Energy and Band Gap Prediction of Titanium Dioxide Based on Machine Learning
inferred← citeshttps://doi.org/10.2139/ssrn.4602549openalex-extracted
System Energy and Band Gap Prediction of Titanium Dioxide Based on Machine Learning
inferred← citeshttps://doi.org/10.1002/adc2.145openalex-snapshot
Correlation analysis and fault detection of current and speed signals for underwater thruster entanglement
inferred← citeshttps://doi.org/10.1002/ente.202200980openalex-snapshot
Automated Machine Learning Approach in Material Discovery of Hole Selective Layers for Perovskite Solar Cells
inferred← citeshttps://doi.org/10.1002/ente.202300735openalex-snapshot
Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives
inferred← citeshttps://doi.org/10.1002/marc.202500711openalex-snapshot
Critical Roles of Polymers in the Development of Flexible Perovskite Solar Cells
inferred← citeshttps://doi.org/10.1002/minf.202300217openalex-snapshot
Predicting the bandgap and efficiency of perovskite solar cells using machine learning methods
inferred← citeshttps://doi.org/10.1007/s11664-026-12774-5openalex-snapshot
PerovLearn: An Interpretable Ensemble Machine Learning Framework for Bandgap Prediction in Hybrid Halide Perovskite Solar Cell Materials
inferred← citeshttps://doi.org/10.1007/s12517-022-11054-wopenalex-snapshot
Watershed prioritization and decision-making based on weighted sum analysis, feature ranking, and machine learning techniques
inferred← citeshttps://doi.org/10.1016/j.ccr.2025.217182openalex-snapshot
Recent advances of theoretical investigation on lead-free metal halide perovskites
inferred← citeshttps://doi.org/10.1016/j.commatsci.2023.112215openalex-snapshot
Machine learning for perovskite solar cell design
inferred← citeshttps://doi.org/10.1016/j.jechem.2025.05.029openalex-snapshot
Perovskite solar cells empowered by machine learning
inferred← citeshttps://doi.org/10.1016/j.jmrt.2022.02.093openalex-snapshot
Experimental investigations and prediction of WEDMed surface of nitinol SMA using SinGAN and DenseNet deep learning model
inferred← citeshttps://doi.org/10.1016/j.jpowsour.2024.235995openalex-snapshot
Enhancing perovskite solar cell efficiency and stability through architectural modifications and additives
inferred← citeshttps://doi.org/10.1016/j.jseaes.2024.106367openalex-snapshot
An improved algorithm with particle swarm optimization-extreme gradient boosting to predict the contents of pyrolytic hydrocarbons in source rocks
inferred← citeshttps://doi.org/10.1016/j.molliq.2025.127315openalex-snapshot
First-principles study on the mechanical and electronic properties of energetic silver-based molecular perovskite
inferred← citeshttps://doi.org/10.1016/j.molstruc.2024.137934openalex-snapshot
System energy and band gap prediction of titanium dioxide based on machine learning
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2023.106376openalex-snapshot
Machine learning-assisted design of wide bandgap perovskite materials for high-efficiency indoor photovoltaic applications
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2023.107026openalex-snapshot
Bandgap prediction of ABX3-type perovskites using Broad Learning System
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2023.107245openalex-snapshot
Rapid discovery of promising materials via active learning with multi-objective optimization
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2024.108945openalex-snapshot
Bond strength between recycled aggregate concrete and rebar: Interpretable machine learning modeling approach for performance estimation and engineering design
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2024.111113openalex-snapshot
Machine learning-driven determination of key absorber layer parameters in perovskite solar cells
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2026.115574openalex-snapshot
A unified machine learning framework for band gap prediction in lead-free ABX₃ and double perovskites
inferred← citeshttps://doi.org/10.1016/j.orgel.2025.107358openalex-snapshot
Machine learning-driven prediction of energy and band gap in FAPbI3 perovskite using diverse structural descriptors
inferred← citeshttps://doi.org/10.1016/j.physleta.2026.132092openalex-snapshot
Mapping coupled composition-processing effects on the strength and ductility of magnesium alloys with interpretable machine learning
inferred← citeshttps://doi.org/10.1016/j.psep.2022.08.029openalex-snapshot
Experimental investigation and validation of ultrasound-assisted extractive/oxidative desulfurization of oil using environmentally benign ionic liquid
inferred← citeshttps://doi.org/10.1016/j.sna.2024.115382openalex-snapshot
BDAPbI4 Dion Jacobson hybrid perovskite-based artificial nociceptors on biodegradable substrate
inferred← citeshttps://doi.org/10.1016/j.solener.2026.114604openalex-snapshot
Machine learning applications in the design, optimization, and manufacturing of perovskite solar cells
inferred← citeshttps://doi.org/10.1021/acs.energyfuels.5c00165openalex-snapshot
Outlook and Future Directions for Indoor Photovoltaics: Modern Societal Needs and Challenges
inferred← citeshttps://doi.org/10.1021/acs.jpclett.4c00959openalex-snapshot
AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity
inferred← citeshttps://doi.org/10.1021/acsomega.2c00878openalex-snapshot
Anisotropic Impact Sensitivity of Metal-Free Molecular Perovskite High-Energetic Material (C6H14N2)(NH2NH3)(ClO4)3 by First-Principles Study
inferred← citeshttps://doi.org/10.1021/acsphotonics.3c01523openalex-snapshot
Machine Learning Regression Analyses of Intensity Modulation Two-Photon Microscopy (ml-IM2PM) in Perovskite Microcrystals
inferred← citeshttps://doi.org/10.1039/d4ra00402gopenalex-snapshot
Predicting band gaps of ABN 3 perovskites: an account from machine learning and first-principle DFT studies
inferred← citeshttps://doi.org/10.1039/d4su00370eopenalex-snapshot
Identifying the best ML model for predicting the bandgap in a perovskite solar cell
inferred← citeshttps://doi.org/10.1039/d5tc04449aopenalex-snapshot
Predicting the bandgap of ABX 2 materials: supervised machine learning on small dataset
inferred← citeshttps://doi.org/10.1088/1361-6463/add020openalex-snapshot
Data-driven prediction of insulation properties in dielectric composites by machine learning
inferred← citeshttps://doi.org/10.1088/1361-648x/ad512dopenalex-snapshot
Predicting photovoltaic parameters of perovskite solar cells using machine learning
inferred← citeshttps://doi.org/10.1088/1402-4896/acae49openalex-snapshot
Hybrid perovskites thin films morphology identification by adapting multiscale-SinGAN architecture, heat transfer search optimized feature selection and machine learning algorithms
inferred← citeshttps://doi.org/10.1088/2631-8695/acdc04openalex-snapshot
RETRACTED: A 3-layered feedforward back-propagation ANN-based SVPWM control for neutral point clamped converter for PV grid integration
inferred← citeshttps://doi.org/10.1088/2631-8695/ae698eopenalex-snapshot
RF-MLP hybrid approach for the positioning and classification of magnetic targets
inferred← citeshttps://doi.org/10.1103/n6dj-hlzmopenalex-snapshot
Generalizable machine-learned interatomic potential for all-inorganic halide double perovskites
inferred← citeshttps://doi.org/10.1109/edkcon62339.2024.10870645openalex-snapshot
Predictive Modeling for Bandgap Tuning in Perovskite Solar Cells Using Machine Learning
inferred← citeshttps://doi.org/10.1139/cjc-2023-0156openalex-snapshot
Predicting the band gap of lead-free inorganic double perovskites using modified parallel residual network
inferred← citeshttps://doi.org/10.1155/2022/9374946openalex-snapshot
Recall Network: A Simple Brain-Inspired Algorithm for Classification
inferred← citeshttps://doi.org/10.1155/er/1215175openalex-snapshot
High‐Accuracy Bandgap Prediction and Classification in Hybrid and Inorganic Halide Perovskites Using Advanced Machine Learning Techniques
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3540876/v1openalex-snapshot
DiZyme: The Ultimate Resource for Nanozyme Multiple Catalytic Activity Prediction
inferred← citeshttps://doi.org/10.2139/ssrn.4902786openalex-snapshot
Deep Learning-Based Prediction of Band Gaps in Large-Cell Three-Dimensional Carbon Allotropes
inferred← citeshttps://doi.org/10.3390/app13095732openalex-snapshot
Equilibrium Optimizer-Based Joint Time-Frequency Entropy Feature Selection Method for Electric Loads in Industrial Scenario
inferred← citeshttps://doi.org/10.3390/ijms241310554openalex-snapshot
Methylammonium Tetrel Halide Perovskite Ion Pairs and Their Dimers: The Interplay between the Hydrogen-, Pnictogen- and Tetrel-Bonding Interactions
inferred← citeshttps://doi.org/10.3390/ma16093371openalex-snapshot
Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network
inferred← citeshttps://doi.org/10.3390/ma17112686openalex-snapshot
Employing the Interpretable Ensemble Learning Approach to Predict the Bandgaps of the Halide Perovskites
inferred← citeshttps://doi.org/10.3390/ma17153741openalex-snapshot
A Perovskite Material Screening and Performance Study Based on Asymmetric Convolutional Blocks
inferred← citeshttps://doi.org/10.3390/mi14050976openalex-snapshot
A Methodology for Enhancing SSVEP Features Using Adaptive Filtering Based on the Spatial Distribution of EEG Signals
inferred← citeshttps://doi.org/10.3390/pr11051486openalex-snapshot
Feature Disentangling Autoencoder for Anomaly Detection of Reactor Core Temperature with Feature Increment Strategy
inferred← citeshttps://doi.org/10.3390/pr13020332openalex-snapshot
Optimization of an Industrial Circulating Water System Based on Process Simulation and Machine Learning
inferred← citeshttps://doi.org/10.3390/s23083833openalex-snapshot
Enhancing Tool Wear Prediction Accuracy Using Walsh–Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection
inferred← citeshttps://doi.org/10.3390/s23083889openalex-snapshot
EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients’ Rehabilitation
inferred← citeshttps://doi.org/10.3390/s23104845openalex-snapshot
Explainability-Informed Feature Selection and Performance Prediction for Nonintrusive Load Monitoring
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.