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Paper Citation Record · LEDGER

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials

As of 20 August 2026, this Paper Citation Record lists 100 of 100 outbound references and 0 inbound Pith citation observations for arXiv:2411.14034.

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pith.paper-citation-record.v1
2411.14034 v1

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Outbound references

Observation f54ee303-90c3-4d72-8241-1e935586a7b1 · outbound

This paper cites From DFT to machine learning: recent approaches to materials science–a review.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials From DFT to machine learning: recent approaches to materials science–a review

Reference 1

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This paper cites Hickman, Mario Krenn, Cyrille Lavigne, Michael Lindner-D’Addario, AkshatKumar Nigam, Cher Tian Ser, Zhenpeng Yao, and Alán Aspuru-Guzik.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Hickman, Mario Krenn, Cyrille Lavigne, Michael Lindner-D’Addario, AkshatKumar Nigam, Cher Tian Ser, Zhenpeng Yao, and Alán Aspuru-Guzik

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Unresolved cited work

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This paper cites The PAULING FILE Project and Materials Platform for Data Science: From Big Data Toward Materials Genome, pages 1–26.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials The PAULING FILE Project and Materials Platform for Data Science: From Big Data Toward Materials Genome, pages 1–26

Reference 4

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This paper cites The open quantum materials database (oqmd): assessing the accuracy of DFT formation energies.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials The open quantum materials database (oqmd): assessing the accuracy of DFT formation energies

Reference 5

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This paper cites Discovery of high-performance dielectric materials with machine-learning-guided search.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Discovery of high-performance dielectric materials with machine-learning-guided search

Reference 6

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This paper cites Conduit, T.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Conduit, T

Reference 7

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This paper cites Oliynyk, Marcus Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Oliynyk, Marcus Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D

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This paper cites Norquist, Tonio Buonassisi, and Jakoah Brgoch.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Norquist, Tonio Buonassisi, and Jakoah Brgoch

Reference 9

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This paper cites Evans, Zhe Li, Ji-Seon Kim, James R.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Evans, Zhe Li, Ji-Seon Kim, James R

Reference 10

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This paper cites Recent progress in transparent conductive materials for photovoltaics.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Recent progress in transparent conductive materials for photovoltaics

Reference 11

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This paper cites Ager, and Christophe Bal- lif.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Ager, and Christophe Bal- lif

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Dwaraknath, and Kristin A

Reference 13

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Xiong, Y

Reference 14

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This paper cites Evaluation of Machine Learning Methods for the Prediction of Key Properties for Novel Transparent Semiconductors.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Evaluation of Machine Learning Methods for the Prediction of Key Properties for Novel Transparent Semiconductors

Reference 15

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Unresolved cited work

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This paper cites The Inorganic Crystal Structure Database ( ICSD ): A Tool for Materials Sciences, pages 41–54.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials The Inorganic Crystal Structure Database ( ICSD ): A Tool for Materials Sciences, pages 41–54

Reference 17

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This paper cites How does chem- istry influence electron effective mass in oxides? a high-throughput computational analysis.Chemistry of Materials, 26(19):5447–5458, 10 2014.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials How does chem- istry influence electron effective mass in oxides? a high-throughput computational analysis.Chemistry of Materials, 26(19):5447–5458, 10 2014

Reference 18

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This paper cites Investigate machine learning methods for transparent conductors prediction, 2018.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Investigate machine learning methods for transparent conductors prediction, 2018

Reference 19

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Antunes, Vikram, Jose J

Reference 20

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Gaultois, Taylor D

Reference 21

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Predicting the band gaps of inorganic solids by machine learning

Reference 22

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This paper cites Accurate prediction of band gap of materials using stacking machine learning model.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Accurate prediction of band gap of materials using stacking machine learning model

Reference 23

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This paper cites Jeffrey Snyder, Gian-Marco Rignanese, Anubhav Jain, and Geoffroy Hautier.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Jeffrey Snyder, Gian-Marco Rignanese, Anubhav Jain, and Geoffroy Hautier

Reference 26

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Garrity, Andrew C

Reference 27

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This paper cites URL https://doi.org/10.1038/sdata.2017.85.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials URL https://doi.org/10.1038/sdata.2017.85

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This paper cites Machine learning based prediction of lattice thermal conductivity for half-heusler compounds using atomic information.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Machine learning based prediction of lattice thermal conductivity for half-heusler compounds using atomic information

Reference 29

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This paper cites Materials informatics platform with three dimensional structures, workflow and thermoelectric applications.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Materials informatics platform with three dimensional structures, workflow and thermoelectric applications

Reference 30

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Data-driven analysis of electron relaxation times in pbte-type thermoelectric materials

Reference 32

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Hart, Michal Jahnatek, Roman V

Reference 33

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Na and Hyunju Chang

Reference 34

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Cohen, Paula Mori-Sánchez, and Weitao Yang

Reference 36

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Clymo, C

Reference 38

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This paper cites Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm

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This paper cites The minimum conductivity and electron localisation in the metallic phase of transition metal compounds in the vicinity of a metal-insulator transition.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials The minimum conductivity and electron localisation in the metallic phase of transition metal compounds in the vicinity of a metal-insulator transition

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This paper cites Effect of indium doping on the uv photoluminescence emission, structural, electrical and optical properties of spin-coating deposited SnO2 thin films.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Effect of indium doping on the uv photoluminescence emission, structural, electrical and optical properties of spin-coating deposited SnO2 thin films

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This paper cites Ranjeth Kumar Reddy, G.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Ranjeth Kumar Reddy, G

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Observation fa319d8f-b204-4288-945f-6e5697404fca · outbound

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Reference 43

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This paper cites Ranjeth Kumar Reddy, Yugandhar Bitla, V.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Ranjeth Kumar Reddy, Yugandhar Bitla, V

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This paper cites Structural, optical and electrical charac- terization of spin coated SnO2:Mn thin films.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Structural, optical and electrical charac- terization of spin coated SnO2:Mn thin films

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Observation 1ab36f5f-2a99-4379-bf12-0d088b905fcc · outbound

This paper cites Enhanced room-temperature ferromagnetism on (In0.98−xCoxSn0.02)2O3 films: magnetic mechanism, optical and transport properties.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Enhanced room-temperature ferromagnetism on (In0.98−xCoxSn0.02)2O3 films: magnetic mechanism, optical and transport properties

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Observation f786b635-99d3-4739-a71c-3c9655acfea8 · outbound

This paper cites The effect of annealing on structural, electrical and optical properties of nanostructured ITO films prepared by e-beam evaporation.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials The effect of annealing on structural, electrical and optical properties of nanostructured ITO films prepared by e-beam evaporation

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This paper cites Poeppelmeier, Melissa Lane, Carl R.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Poeppelmeier, Melissa Lane, Carl R

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This paper cites Trans- parent and conductive W-doped SnO2 thin films fabricated by an aqueous solution process.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Trans- parent and conductive W-doped SnO2 thin films fabricated by an aqueous solution process

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This paper cites Shukla, Anchal Srivastava, Atul Srivastava, and K.C.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Shukla, Anchal Srivastava, Atul Srivastava, and K.C

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This paper cites Ajimsha, Amit K.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Ajimsha, Amit K

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Observation ca2a4c6b-80b0-4fb8-8ac3-0ce7f52fb4eb · outbound

This paper cites Random forests.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Random forests

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This paper cites Biphase effect on structural, optical, and electrical properties of Al-Sn codoped ZnO thin films deposited by sol-gel spin-coating technique.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Biphase effect on structural, optical, and electrical properties of Al-Sn codoped ZnO thin films deposited by sol-gel spin-coating technique

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This paper cites Probabilistic Data-Driven Discovery of Thermoelectric Materials.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Probabilistic Data-Driven Discovery of Thermoelectric Materials

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Observation 78d5aad7-51d4-4710-8efc-ca0f148d9908 · outbound

This paper cites Prediction of mechanical strength by using an artificial neural network and random forest algorithm.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Prediction of mechanical strength by using an artificial neural network and random forest algorithm

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This paper cites A general-purpose machine learning framework for predicting properties of inorganic materials.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials A general-purpose machine learning framework for predicting properties of inorganic materials

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Reference 57

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This paper cites Kauwe, Ryan J.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Kauwe, Ryan J

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This paper cites The utility of composition-based machine learning models for band gap prediction.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials The utility of composition-based machine learning models for band gap prediction

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Baird, Tran Q

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This paper cites Hargreaves, Michael W.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Hargreaves, Michael W

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This paper cites Predicting thermoelectric transport properties from composition with attention-based deep learning.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Predicting thermoelectric transport properties from composition with attention-based deep learning

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This paper cites CLCS : Contrastive learning between compositions and structures for practical Li-ion battery electrodes design.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials CLCS : Contrastive learning between compositions and structures for practical Li-ion battery electrodes design

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This paper cites Attention Is All You Need.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Attention Is All You Need

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This paper cites The Elements of Statistical Learning.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials The Elements of Statistical Learning

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This paper cites Can machine learning identify the next high-temperature superconductor? examining extrapolation performance for materials discovery.Mol.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Can machine learning identify the next high-temperature superconductor? examining extrapolation performance for materials discovery.Mol

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Gusev, and Taylor D

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Observation d846bb6b-b098-4773-88be-de7479f16df5 · outbound

This paper cites A critical examination of robustness and generalizability of machine learning prediction of materials properties.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials A critical examination of robustness and generalizability of machine learning prediction of materials properties

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This paper cites Structure-based out-of-distribution (ood) materials property prediction: a benchmark study, 2024.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Structure-based out-of-distribution (ood) materials property prediction: a benchmark study, 2024

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Observation fc2e72d5-9b50-493b-8078-57942ddef49e · outbound

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Observation efa8ac03-a229-4edf-98a6-6bd73a3d01d2 · outbound

This paper cites Predicting the electrical conductivity of brine-saturated rocks using machine learning methods.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Predicting the electrical conductivity of brine-saturated rocks using machine learning methods

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This paper cites RBF kernel based support vector machine with universal approximation and its application.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials RBF kernel based support vector machine with universal approximation and its application

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Observation d7a3f8fb-cadb-4765-826c-9d7addc75fb2 · outbound

This paper cites Kitchin, Zachary W.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Kitchin, Zachary W

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Observation f571a60b-e93e-420f-ab2c-cd82f8ed5395 · outbound

This paper cites Leveraging large language models for predictive chemistry.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Leveraging large language models for predictive chemistry

Reference 76

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Observation 9e682589-7126-48f1-bd42-43b84b49757c · outbound

This paper cites The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation

Reference 77

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Observation f493510f-3763-44e5-89ed-7b63240e7367 · outbound

This paper cites Explainable AI: A review of machine learning interpretability methods.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Explainable AI: A review of machine learning interpretability methods

Reference 78

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Observation 17ebc3df-4fbd-4229-bcc5-c7584a3b2e59 · outbound

This paper cites Gaultois, Vladimir V.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Gaultois, Vladimir V

Reference 79

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Observation 589f19ae-a887-4d68-ad66-1bea52158c68 · outbound

This paper cites Effect of Na doping on microstructures, optical and electrical properties of ZnO thin films grown by sol-gel method.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Effect of Na doping on microstructures, optical and electrical properties of ZnO thin films grown by sol-gel method

Reference 80

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Observation dea1a9ed-b56d-4cbc-8454-2cf0eafb3b23 · outbound

This paper cites Enhanced gas sensing properties of spin-coated Na-doped ZnO nanostructured films.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Enhanced gas sensing properties of spin-coated Na-doped ZnO nanostructured films

Reference 81

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Observation 303835dc-aed9-44a7-bc25-2cca64f697db · outbound

This paper cites Synthesis and characteristics ofCa-doped ZnO thin films by rf magnetron sputtering at low temperature.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Synthesis and characteristics ofCa-doped ZnO thin films by rf magnetron sputtering at low temperature

Reference 82

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Observation 56cc46a9-9fc0-4d5d-8b38-bd3713597b15 · outbound

This paper cites Subsolidus phase relations in the Ga2O3-In2O3-SnO2 system.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Subsolidus phase relations in the Ga2O3-In2O3-SnO2 system

Reference 83

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Observation bdcc3d00-98ad-4225-98da-3706b01895e9 · outbound

This paper cites A new transparent conducting oxide in the Ga2O3-In2O3-SnO2 system.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials A new transparent conducting oxide in the Ga2O3-In2O3-SnO2 system

Reference 84

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Observation 6df6a7a7-d264-4adb-9034-78ec7c262954 · outbound

This paper cites Phase stability and optoelec- tronic properties of the bixbyite phase in the gallium–indium–tin–oxide system.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Phase stability and optoelec- tronic properties of the bixbyite phase in the gallium–indium–tin–oxide system

Reference 85

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Observation 25ebe923-e198-4e5a-94ac-f61e5e4de0f3 · outbound

This paper cites Attention is all you need.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Attention is all you need

Reference 86

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Observation 6fff8997-6c0e-469a-9066-26319fb9cc2b · outbound

This paper cites Al-doped zinc stannate films for photovoltaic applications.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Al-doped zinc stannate films for photovoltaic applications

Reference 87

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This paper cites Mechanism of electrical conductivity of transparent InGaZnO4.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Mechanism of electrical conductivity of transparent InGaZnO4

Reference 88

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Observation 13b7a86c-005e-4958-aa1c-aa0f01d1d0d7 · outbound

This paper cites Carrier generation in multicomponent wide-bandgap oxides: InGaZnO 4.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Carrier generation in multicomponent wide-bandgap oxides: InGaZnO 4

Reference 89

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Observation 49b55594-a07e-4540-81ee-f7ecbc3639be · outbound

This paper cites Kauwe, Jake Graser, Ryan Murdock, and Taylor D.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Kauwe, Jake Graser, Ryan Murdock, and Taylor D

Reference 90

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Observation 0ba1f0e4-dda6-4684-a83a-1cf575df0b47 · outbound

This paper cites Large language models as master key: unlocking the secrets of materials science.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Large language models as master key: unlocking the secrets of materials science

Reference 91

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Observation 5e528b86-f83f-4c31-a94b-8a36ce8f4562 · outbound

This paper cites Compositional Representation of Polymorphic Crystalline Materials.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Compositional Representation of Polymorphic Crystalline Materials

Reference 92

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Observation 3ba4c365-39d6-48bc-9dc3-1d59845e4254 · outbound

This paper cites Utilizing a unified structure model in (3+1)-dimensional superspace to identify a homologous phase (Ga1−αAlα)2O3(ZnO)m in ZnO-based thermoelectric composites.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Utilizing a unified structure model in (3+1)-dimensional superspace to identify a homologous phase (Ga1−αAlα)2O3(ZnO)m in ZnO-based thermoelectric composites

Reference 93

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Observation cb020fae-4af6-40a6-886b-67e7e21e5752 · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 94

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Observation f8f42239-6dec-45fe-b4bd-c9b84ac22cf3 · outbound

This paper cites A Primer on Bayesian Neural Networks: Review and Debates.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials A Primer on Bayesian Neural Networks: Review and Debates

Reference 95

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Observation 5627231b-360f-418a-a95b-b56ddbaade07 · outbound

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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials Unresolved cited work

Reference 100

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Observation d9b063d1-67ef-4592-81e3-cbda2b39c512 · outbound

This paper cites URL https://onlinelibrary.wiley.com/doi/abs/10.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials URL https://onlinelibrary.wiley.com/doi/abs/10

Reference 2017

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Observation af6872d3-5911-408f-a2c9-e28d1294df35 · outbound

This paper cites doi:https://doi.org/10.1016/j.ijleo.2018.01.025.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials doi:https://doi.org/10.1016/j.ijleo.2018.01.025

Reference 2018

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Observation 8e5b6f24-dd97-4922-b833-6e2a92ce5ad2 · outbound

This paper cites URL https://dx.doi.org/10.1088/2632-2153/acc4a9.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials URL https://dx.doi.org/10.1088/2632-2153/acc4a9

Reference 2023

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Observation 02675201-e305-4450-84ed-9188163436fb · outbound

This paper cites URL https://www.sciencedirect.com/science/ article/pii/S0030402620316764.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials URL https://www.sciencedirect.com/science/ article/pii/S0030402620316764

Reference 4026

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Observation e01e9a2a-0252-41c1-a6ea-8aef3d2cd79e · outbound

This paper cites URL https://www.sciencedirect.com/science/ article/pii/S2214785320333988.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials URL https://www.sciencedirect.com/science/ article/pii/S2214785320333988

Reference 7853

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a4500737-3b10-4790-8e16-357820e0dbf9 · outbound

This paper cites URL https://www.sciencedirect.com/science/ article/pii/S0925838815006027.

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials URL https://www.sciencedirect.com/science/ article/pii/S0925838815006027

Reference 8388

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verified exact
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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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