Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials | Paper Citation Record

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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

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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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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

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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

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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

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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

Reference 8

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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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Observation 8b64bac0-951e-4f03-921f-4243633a7a70 · outbound

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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Observation 08f8ae78-3cf1-4ca7-bed9-ad25130d4de9 · outbound

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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Observation 873ba21d-3ffa-47b5-8d8f-54b06a40700a · outbound

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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This paper cites Dwaraknath, and Kristin A.

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

Reference 13

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This paper cites Xiong, Y.

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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Reference 16

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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

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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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Observation 463a35f5-ad85-4aba-a77e-21329d86d8a5 · outbound

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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This paper cites Antunes, Vikram, Jose J.

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

Reference 20

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Observation ddf3dc95-70e0-4b3f-92a0-3fde073cd730 · outbound

This paper cites Gaultois, Taylor D.

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

Reference 21

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This paper cites Predicting the band gaps of inorganic solids by machine learning.

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

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This paper cites Garrity, Andrew C.

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

Reference 28

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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

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This paper cites Data-driven analysis of electron relaxation times in pbte-type thermoelectric materials.

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

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

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

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This paper cites Cohen, Paula Mori-Sánchez, and Weitao Yang.

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

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

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

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

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

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

Reference 64

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

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

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

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

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

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

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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

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

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

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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

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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

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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

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

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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

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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

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

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

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

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

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

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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

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

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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

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

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

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

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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

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

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

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Reference 8388

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