On the public citation graph

Deep Learning-Based Prediction of Band Gaps in Large-Cell Three-Dimensional Carbon Allotropes

https://doi.org/10.2139/ssrn.4902786
0 signed · 50 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/aenm.201601574openalex-snapshot
Carbon Photodetectors: The Versatility of Carbon Allotropes
inferredcites →https://doi.org/10.1002/aenm.201703241openalex-snapshot
Advances in Carbon Nanotube–Silicon Heterojunction Solar Cells
inferredcites →https://doi.org/10.1002/qute.201900023openalex-snapshot
Band Gap Prediction for Large Organic Crystal Structures with Machine Learning
inferredcites →https://doi.org/10.1002/rcm.1290060313openalex-snapshot
C 60 and C 70 fullerene isomers generated in flames. Detection and verification by liquid chromatography/mass spectrometry analyses
inferredcites →https://doi.org/10.1002/solr.202100927openalex-snapshot
Predicting Perovskite Bandgap and Solar Cell Performance with Machine Learning
inferredcites →https://doi.org/10.1016/0010-4655(87)90053-1openalex-snapshot
General Hartree-Fock program
inferredcites →https://doi.org/10.1016/j.carbon.2008.08.027openalex-snapshot
Enhanced device performance using different carbon nanotube types in polymer photovoltaic devices
inferredcites →https://doi.org/10.1016/j.cjph.2023.06.013openalex-snapshot
A novel three-dimensional superhard carbon allotrope with a direct band gap
inferredcites →https://doi.org/10.1016/j.commatsci.2010.05.010openalex-snapshot
High-throughput electronic band structure calculations: Challenges and tools
inferredcites →https://doi.org/10.1016/j.commatsci.2018.05.018openalex-snapshot
Matminer: An open source toolkit for materials data mining
inferredcites →https://doi.org/10.1016/j.commatsci.2021.110637openalex-snapshot
The utility of composition-based machine learning models for band gap prediction
inferredcites →https://doi.org/10.1016/j.commatsci.2021.110899openalex-snapshot
Accurate prediction of band gap of materials using stacking machine learning model
inferredcites →https://doi.org/10.1016/j.compscitech.2019.04.028openalex-snapshot
Carbon-based functional nanomaterials: Preparation, properties and applications
inferredcites →https://doi.org/10.1016/j.diamond.2020.107990openalex-snapshot
Electronic band structure phase diagram of 3D carbon allotropes from machine learning
inferredcites →https://doi.org/10.1016/j.diamond.2023.109854openalex-snapshot
Four superhard tetragonal carbon allotropes: First-principles calculations
inferredcites →https://doi.org/10.1016/j.diamond.2024.111320openalex-snapshot
Explorative prediction of novel superhard carbon allotropes with lager cell: Density functional theory-assisted deep learning
inferredcites →https://doi.org/10.1016/j.jmat.2022.04.006openalex-snapshot
Prediction of nature of band gap of perovskite oxides (ABO3) using a machine learning approach
inferredcites →https://doi.org/10.1016/j.mtadv.2024.100486openalex-snapshot
High-throughput design of three-dimensional carbon allotropes with Pmna space group
inferredcites →https://doi.org/10.1016/j.physleta.2021.127800openalex-snapshot
Bandgap prediction of metal halide perovskites using regression machine learning models
inferredcites →https://doi.org/10.1016/s0038-1098(98)00215-4openalex-snapshot
Light emission from semiconducting polymers: Light-emitting diodes, light-emitting electrochemical cells, lasers and white light for the future
inferredcites →https://doi.org/10.1016/s0965-9773(97)00016-0openalex-snapshot
Nanotechnology in carbon materials
inferredcites →https://doi.org/10.1021/acs.cgd.3c00479openalex-snapshot
BN Polymorphs in Hexagonal 2–7 Stacking Orders: First-Principles and High-Throughput Study
inferredcites →https://doi.org/10.1021/acs.jpclett.8b00124openalex-snapshot
Predicting the Band Gaps of Inorganic Solids by Machine Learning
inferredcites →https://doi.org/10.1021/acsnano.5b01848openalex-snapshot
Stretchable Carbon Nanotube Charge-Trap Floating-Gate Memory and Logic Devices for Wearable Electronics
inferredcites →https://doi.org/10.1021/co200012wopenalex-snapshot
High-Throughput Combinatorial Database of Electronic Band Structures for Inorganic Scintillator Materials
inferredcites →https://doi.org/10.1021/ic501362bopenalex-snapshot
High-Throughput ab Initio Screening for Two-Dimensional Electride Materials
inferredcites →https://doi.org/10.1021/nl070961copenalex-snapshot
Double-Walled Carbon Nanotube Solar Cells
inferredcites →https://doi.org/10.1038/s41524-021-00585-7openalex-snapshot
Machine learning and evolutionary prediction of superhard B-C-N compounds
inferredcites →https://doi.org/10.1038/s43246-023-00373-4openalex-snapshot
Band gap predictions of double perovskite oxides using machine learning
inferredcites →https://doi.org/10.1039/d1ta07553eopenalex-snapshot
High-throughput computation of novel ternary B–C–N structures and carbon allotropes with electronic-level insights into superhard materials from machine learning
inferredcites →https://doi.org/10.1063/1.1760074openalex-snapshot
Efficient hybrid density functional calculations in solids: Assessment of the Heyd–Scuseria–Ernzerhof screened Coulomb hybrid functional
inferredcites →https://doi.org/10.1063/1.2085170openalex-snapshot
Energy band gaps and lattice parameters evaluated with the Heyd-Scuseria-Ernzerhof screened hybrid functional
inferredcites →https://doi.org/10.1063/1.2404663openalex-snapshot
Influence of the exchange screening parameter on the performance of screened hybrid functionals
inferredcites →https://doi.org/10.1063/1.4722993openalex-snapshot
Analysis of the Heyd-Scuseria-Ernzerhof density functional parameter space
inferredcites →https://doi.org/10.1088/1674-1056/ab9c01openalex-snapshot
tP40 carbon: A novel superhard carbon allotrope*
inferredcites →https://doi.org/10.1088/2516-1075/abbb25openalex-snapshot
High-throughput design of magnetic materials
inferredcites →https://doi.org/10.1093/nar/gkaa439openalex-snapshot
Native de novo structural determinations of non-canonical nucleic acid motifs by X-ray crystallography at long wavelengths
inferredcites →https://doi.org/10.1103/physrev.81.385openalex-snapshot
A Simplification of the Hartree-Fock Method
inferredcites →https://doi.org/10.1103/physrevb.13.5188openalex-snapshot
Special points for Brillouin-zone integrations
inferredcites →https://doi.org/10.1103/physrevb.77.115123openalex-snapshot
Fractional charge perspective on the band gap in density-functional theory
inferredcites →https://doi.org/10.1103/physrevb.93.115104openalex-snapshot
Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques
inferredcites →https://doi.org/10.1103/physrevb.97.014104openalex-snapshot
Stochastic generation of complex crystal structures combining group and graph theory with application to carbon
inferredcites →https://doi.org/10.1103/physrevlett.105.196403openalex-snapshot
Efficient Band Gap Prediction for Solids
inferredcites →https://doi.org/10.1103/physrevlett.121.175701openalex-snapshot
Complex Low Energy Tetrahedral Polymorphs of Group IV Elements from First Principles
inferredcites →https://doi.org/10.1103/physrevlett.77.3865openalex-snapshot
Generalized Gradient Approximation Made Simple
inferredcites →https://doi.org/10.1103/revmodphys.73.515openalex-snapshot
Phonons and related crystal properties from density-functional perturbation theory
inferredcites →https://doi.org/10.1109/ciep.2018.8573377openalex-snapshot
Hybrid Energy Storage Systems for Electric Vehicles: Multi-Source Inverter Topologies
inferredcites →https://doi.org/10.1371/journal.pone.0255637openalex-snapshot
Bandgap prediction of two-dimensional materials using machine learning
inferredcites →https://doi.org/10.2139/ssrn.4637529openalex-snapshot
Accelerate the Design of New Superhard Carbon Allotropes in the Pca21 Space Group: High-Throughput Screening and Machine Learning Strategies
inferredcites →https://doi.org/10.3390/ijms20040976openalex-snapshot
Materials for Photovoltaics: State of Art and Recent Developments
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.