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Integrated classical/quantum–classical neural networks, DFT, and experiments for predicting bandgap and color of CrxSbxTi1−2xO2 yellow-orange pigments
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inferredcites →https://doi.org/10.1007/bf00357354openalex-snapshot
Synthesis of CrxTi1−2xNbxO2O20≤x≤0.5 rutile solid solutions from alkoxides
inferredcites →https://doi.org/10.1007/s00339-024-07309-wopenalex-snapshot
Study the effect of doping cobalt ions into Zn(2−x)CoxSiO4 [x = 0, 0.056 and 0.167] structures on the optical properties and color parameters: experiment and calculation
inferredcites →https://doi.org/10.1007/s10973-012-2901-6openalex-snapshot
Anatase–rutile transformation at the synthesis of rutile pigments (Ti,Cr,Nb)O2 and their color properties
inferredcites →https://doi.org/10.1007/s10973-013-3606-1openalex-snapshot
The influence of concentration of doping elements on anatase–rutile transformation at the synthesis of rutile pigments (Ti,Cr,Nb)O2 and their pigmentary properties
inferredcites →https://doi.org/10.1016/j.cej.2011.05.021openalex-snapshot
Cr-doped perovskite and rutile pigments derived from industrial by-products
inferredcites →https://doi.org/10.1016/j.ceramint.2005.03.014openalex-snapshot
The role of counterions (Mo, Nb, Sb, W) in Cr-, Mn-, Ni- and V-doped rutile ceramic pigments
inferredcites →https://doi.org/10.1016/j.commatsci.2012.10.028openalex-snapshot
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
inferredcites →https://doi.org/10.1016/j.commatsci.2016.10.015openalex-snapshot
Band structure diagram paths based on crystallography
inferredcites →https://doi.org/10.1016/j.commatsci.2025.113694openalex-snapshot
Quantum machine learning for ABO3 perovskite structure prediction
inferredcites →https://doi.org/10.1016/j.cpc.2022.108455openalex-snapshot
HP – A code for the calculation of Hubbard parameters using density-functional perturbation theory
inferredcites →https://doi.org/10.1016/j.cplett.2008.12.086openalex-snapshot
Cr/Sb co-doped TiO2 from first principles calculations
inferredcites →https://doi.org/10.1016/j.dyepig.2012.12.004openalex-snapshot
Low temperature preparation of Cr-doped rutile pigments with good colour properties
inferredcites →https://doi.org/10.1016/j.dyepig.2014.05.009openalex-snapshot
Highly dispersed (Cr, Sb)-co-doped rutile pigments of cool color with high near-infrared reflectance
inferredcites →https://doi.org/10.1016/j.dyepig.2015.01.016openalex-snapshot
Pigments based on Cr and Sb doped TiO 2 prepared by microemulsion-mediated solvothermal synthesis for inkjet printing on ceramics
inferredcites →https://doi.org/10.1016/j.dyepig.2016.06.034openalex-snapshot
Cr- and Sb-containing TiO 2 inorganic orange nano-pigments prepared by a relative long hydrocarbon chain polyol
inferredcites →https://doi.org/10.1016/j.dyepig.2016.12.020openalex-snapshot
Hydrothermal-mediated synthesis of orange Cr, Sb-containing TiO 2 nano-pigments with improved microstructure
inferredcites →https://doi.org/10.1016/j.jallcom.2018.11.114openalex-snapshot
Preparation of (Fe,Cr)2O3@TiO2 cool pigments for energy saving applications
inferredcites →https://doi.org/10.1016/j.jeurceramsoc.2003.07.012openalex-snapshot
Structure and color of Ni x A 1−3x B 2x O 2 (A=Ti, Sn; B=Sb, Nb) solid solutions
inferredcites →https://doi.org/10.1016/j.jpcs.2019.109244openalex-snapshot
Fe-doped titanite pigment: Synthesis, DFT/TDDFT calculations by Lanczos and Bethe-Salpeter equation methods and comparison of computational and experimental color properties
inferredcites →https://doi.org/10.1016/j.jpcs.2020.109402openalex-snapshot
A joint experimental and theoretical study on the structural, electronic and optical properties of malayaite and Chromium‐doped malayaite structures as pigments
inferredcites →https://doi.org/10.1016/j.jssc.2006.11.026openalex-snapshot
Synthesis and characterization of Ti1−2xNbxNixO2−x/2 solid solutions
inferredcites →https://doi.org/10.1016/j.jssc.2023.124463openalex-snapshot
Structural, electronic, and optical properties of CaCoSi2O6 and Ca2CoSi2O7 blue pigments: A combined experimental, DFT+U, and BSE study
inferredcites →https://doi.org/10.1016/j.partic.2017.12.016openalex-snapshot
Synthesis and characterization of (Ni, Sb)-co-doped rutile ceramic pigment via mechanical activation-assisted solid-state reaction
inferredcites →https://doi.org/10.1016/j.powtec.2021.05.070openalex-snapshot
Experimental and theoretical study on the structural, electronic, and optical properties within DFT+U, F kernel for LRC model, and BSE approaches. Part I: CoTiO3 and Co2TiO4 pigments
inferredcites →https://doi.org/10.1016/j.powtec.2021.11.043openalex-snapshot
Experimental and theoretical study on the structural, electronic, and optical properties within DFT+U, F kernel for LRC model, and BSE approaches. Part II: CoSiO3 and Co2SiO4 pigments
inferredcites →https://doi.org/10.1016/j.rinp.2020.102995openalex-snapshot
Structural and optical properties of Mg-substituted LaFeO3 nanoparticles prepared by a sol-gel method
inferredcites →https://doi.org/10.1016/j.rinp.2024.107653openalex-snapshot
Temperature-dependent optical bandgap of TiO2 under the Anatase and Rutile phases
inferredcites →https://doi.org/10.1021/acs.jpclett.3c01703openalex-snapshot
Quantum Machine Learning in Materials Prediction: A Case Study on ABO3 Perovskite Structures
inferredcites →https://doi.org/10.1038/nature23879openalex-snapshot
Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
inferredcites →https://doi.org/10.1038/s41598-024-59276-0openalex-snapshot
Practical application of quantum neural network to materials informatics
inferredcites →https://doi.org/10.1038/s42254-021-00348-9openalex-snapshot
Variational quantum algorithms
inferredcites →https://doi.org/10.1063/1.4812323openalex-snapshot
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
inferredcites →https://doi.org/10.1080/21870764.2020.1870260openalex-snapshot
Water-based ink-jet ink based on nano Ni and Sb doped TiO2prepared for printing on ceramics
inferredcites →https://doi.org/10.1088/2058-9565/ab4eb5openalex-snapshot
Parameterized quantum circuits as machine learning models
inferredcites →https://doi.org/10.1103/physreva.103.032430openalex-snapshot
Effect of data encoding on the expressive power of variational quantum-machine-learning models
inferredcites →https://doi.org/10.1103/physrevlett.122.040504openalex-snapshot
Quantum Machine Learning in Feature Hilbert Spaces
inferredcites →https://doi.org/10.1107/s0021889811038970openalex-snapshot
VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data
inferredcites →https://doi.org/10.1107/s1600576715002319openalex-snapshot
QUALX2.0 : a qualitative phase analysis software using the freely available database POW_COD
inferredcites →https://doi.org/10.1179/096797804225012710openalex-snapshot
Study of nickel precursors in (Ni,M,Ti)O 2 (M = Sb, Nb) yellow ceramic pigments
inferredcites →https://doi.org/10.3390/ma15062049openalex-snapshot
Insight into the Effect of Counterions on the Chromatic Properties of Cr-Doped Rutile TiO2-Based Pigments
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