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RESEARCH PAPER · Inorganic Chemistry · 2019

Solving the Coloring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation

Alexander S. Gzyl, Anton O. Oliynyk, Lawrence Asamoah Adutwum, Arthur Mar

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29 direct citing papers in this saved network. Explore the next layer with second-order citations.

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42 direct citing works

Solving the Coloring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental ValidationOriginalNotizen aus der Chemie0Virtual Issue on Machine-Learning Discoveries in Materials Science29Twenty years of exceptional success: The molecular education and research consortium in undergraduate computational chemistry ( MERCURY )29Comparison of computational and experimental inorganic crystal structures37Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation44Materials discovery through machine learning formation energy77Periodic TableTalks: The Elements Never Go Out of Style2Rapid discovery of narrow bandgap oxide double perovskites using machine learning59Machine Learning in Solid‐State Chemistry: Heusler Compounds0Machine-learning enabled thermodynamic model for the design of new rare-earth compounds35Design of Experiment: A Rational and Still Unexplored Approach to Inorganic Materials’ Synthesis15Green Chemistry Applied to Transition Metal Chalcogenides through Synthesis, Design of Experiments, Life Cycle Assessment, and Machine Learning5True colours shining through: Determining site distributions in coloured Li-containing quaternary Heusler compounds3Data-driven materials discovery and synthesis using machine learning methods14Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd36Screening New Quaternary Semiconductor Heusler Compounds By Machine-Learning Methods8Design of Heusler alloys using Co-VB-Sn (VB = V, Nb, Ta) atoms within ab initio calculations for thermoelectric applications0Machine Learning-Based Predictions for Half-Heusler Phases11Isopointal intermetallics: the cP24, dca phases as a representative set of examples, along with their vacancy-ordered variants β-Mn and SrSi26Machine Learning-Based Predictions of Power Factor for Half-Heusler Phases5Compositional optimization of rare earth permanent magnetic alloy by machine learning2Composition and structure analyzer/featurizer for explainable machine-learning models to predict solid state structures13Recent strides in artificial intelligence for predicting thermoelectric properties and materials discovery9Unsupervised Machine Learning Prediction of a Novel 1:3 Intermetallic Phase with the Synthesis of TbIr 3 (PuNi 3 -type) as Experimental Validation7Materials Informatics Tools to Analyze Crystal Structures: Crystal Structure of the Novel Ternary Indide ErCo2In3Prediction of ABX Compounds (X = As, Sn, Sb, Pb, or Bi) with the MgAgAs Structure Type and Their Crystal Lattice Parameters0Investigating mechanical properties through defect chemistry in hard binary phosphide material Ta3P0Machine learning–driven thermoelectric materials: Review on prediction, optimization, and discovery2Disentangling Structural Patterns in Quaternary Germanides with Materials Informatics and Exploratory Synthesis: RE 4 IrInGe 4 ( RE = Y, Ce–Nd, Sm, Gd, Ho–Er) Series1

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  1. Notizen aus der Chemie

    Nachrichten aus der Chemie · 2019-09-01

    Guillaume Delaittre, Johanna Heine, Constantin Hoch, Ullrich Jahn, Stefan Knecht, Hajo Kries, Björn Meermann, Andreas Schnepf, Carl Christoph Tzschucke, Markus Zegke

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  2. Virtual Issue on Machine-Learning Discoveries in Materials Science

    Chemistry of Materials · 2019-10-22

    Anton O. Oliynyk, Jillian M. Buriak

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  3. Twenty years of exceptional success: The molecular education and research consortium in undergraduate computational chemistry ( MERCURY )

    International Journal of Quantum Chemistry · 2020-06-01

    George C. Shields

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  4. Comparison of computational and experimental inorganic crystal structures

    Journal of Solid State Chemistry · 2020-07-19

    Jan‐Hendrik Pöhls, Manon Heyberger, A. Mar

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  5. Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation

    Crystal Growth & Design · 2020-09-01

    Alexander S. Gzyl, Anton O. Oliynyk, Arthur Mar

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  6. Materials discovery through machine learning formation energy

    Journal of Physics Energy · 2021-02-09

    Gordon G. C. Peterson, Jakoah Brgoch

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  7. Periodic TableTalks: The Elements Never Go Out of Style

    Inorganic Chemistry · 2021-04-26

    Javier Vela, Emily A. Weiss

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  8. Rapid discovery of narrow bandgap oxide double perovskites using machine learning

    Computational Materials Science · 2021-04-28

    Xue Yang, Long Li, Qiuling Tao, Wencong Lu, Minjie Li

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  9. Machine Learning in Solid‐State Chemistry: Heusler Compounds

    Encyclopedia of Inorganic and Bioinorganic Chemistry · 2021-06-17

    Alexander S. Gzyl, Arthur Mar, Anton O. Oliynyk

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  10. Machine-learning enabled thermodynamic model for the design of new rare-earth compounds

    Acta Materialia · 2022-02-22

    Prashant Kumar Singh, Tyler J. Del Rose, Guillermo Vazquez, Raymundo Arróyave, Yaroslav Mudryk

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  11. Design of Experiment: A Rational and Still Unexplored Approach to Inorganic Materials’ Synthesis

    Sustainable Chemistry · 2022-03-06

    Francesco Lamberti, Chiara Mazzariol, Federico Spolaore, Riccardo Ceccato, Luigi Salmaso, Silvia Gross

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  12. Green Chemistry Applied to Transition Metal Chalcogenides through Synthesis, Design of Experiments, Life Cycle Assessment, and Machine Learning

    IntechOpen eBooks · 2022-05-20

    Alexandre H. Pinto, Dylan R. Cho, Anton O. Oliynyk, Julian R. Silverman

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  13. True colours shining through: Determining site distributions in coloured Li-containing quaternary Heusler compounds

    Journal of Solid State Chemistry · 2022-06-28

    Mohammed Jomaa, Vidyanshu Mishra, Madhusudan Chaudhary, Dundappa Mumbaraddi, Vladimir K. Michaelis, A. Mar

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  14. Data-driven materials discovery and synthesis using machine learning methods

    Elsevier eBooks · 2022-11-04

    Sterling G. Baird, Marianne Liu, Hasan Muhammad Sayeed, Taylor D. Sparks

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  15. Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd3

    ChemRxiv · 2023-01-03

    Sangjoon Lee, Clio Chen, Griheydi Garcia, Anton O. Oliynyk

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  16. Screening New Quaternary Semiconductor Heusler Compounds By Machine-Learning Methods

    Chemistry of Materials · 2023-09-08

    Runan Xie, Jean‐Claude Crivello, Céline Barreteau

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  17. Design of Heusler alloys using Co-VB-Sn (VB = V, Nb, Ta) atoms within ab initio calculations for thermoelectric applications

    Unknown journal · 2023-11-07

    ALAN Antônio das Graças SANTOS

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  18. Machine Learning-Based Predictions for Half-Heusler Phases

    Inorganics · 2023-12-22

    Kaja Bilińska, Maciej J. Winiarski

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  19. Isopointal intermetallics: the cP24, dca phases as a representative set of examples, along with their vacancy-ordered variants β-Mn and SrSi2

    Zeitschrift für Naturforschung B · 2024-02-01

    Oliver Janka, Rainer Pöttgen

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  20. Machine Learning-Based Predictions of Power Factor for Half-Heusler Phases

    Crystals · 2024-04-09

    Kaja Bilińska, Maciej J. Winiarski

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  21. Compositional optimization of rare earth permanent magnetic alloy by machine learning

    Journal of Magnetism and Magnetic Materials · 2024-11-23

    Lingyao Zhang, Tianhao Su, Yaning Cui, Guanhua Qin, Shihui Yan, Shunbo Hu, Xu Sun, Yang Li, Hang Su, Wei Li Ren

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  22. Composition and structure analyzer/featurizer for explainable machine-learning models to predict solid state structures

    Digital Discovery · 2025-01-01

    Emil I. Jaffal, Sangjoon Lee, Danila Shiryaev, Alex Vtorov, Nikhil Kumar Barua, Holger Kleinke, Anton O. Oliynyk

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  23. Recent strides in artificial intelligence for predicting thermoelectric properties and materials discovery

    Journal of Physics Energy · 2025-02-26

    Nikhil Kumar Barua, Sangjoon Lee, Anton O. Oliynyk, Holger Kleinke

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  24. Unsupervised Machine Learning Prediction of a Novel 1:3 Intermetallic Phase with the Synthesis of TbIr 3 (PuNi 3 -type) as Experimental Validation

    Journal of the American Chemical Society · 2025-04-18

    Siddha Sankalpa Sethi, Arnab Dutta, Emil I. Jaffal, Nishant Yadav, Danila Shiryaev, Brian Hoang, Anirudh Machathi, Sangjoon Lee, Karabi Das, Partha P. Jana, Anton O. Oliynyk

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  25. Materials Informatics Tools to Analyze Crystal Structures: Crystal Structure of the Novel Ternary Indide ErCo2In

    Integrating materials and manufacturing innovation · 2025-06-01

    Yu. Tyvanchuk, Sangjoon Lee, Volodymyr Babizhetskyy, Volodymyr Smetana, Anja‐Verena Mudring, Mariya V. Dzevenko, Emil I. Jaffal, Balaranjan Selvaratnam, Anton O. Oliynyk

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  26. Prediction of ABX Compounds (X = As, Sn, Sb, Pb, or Bi) with the MgAgAs Structure Type and Their Crystal Lattice Parameters

    Inorganic Materials Applied Research · 2025-09-24

    N. N. Kiseleva, V. A. Dudarev, A. V. Stolyarenko, Oleg Valentinovich Sen'ko, A. A. Dokukin, Yu. O. Kuznetsova

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  27. Investigating mechanical properties through defect chemistry in hard binary phosphide material Ta3P

    Solid State Communications · 2025-11-20

    Emil I. Jaffal, Danila Shiryaev, Balaranjan Selvaratnam, Anton O. Oliynyk

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  28. Machine learning–driven thermoelectric materials: Review on prediction, optimization, and discovery

    Journal of Alloys and Compounds · 2025-12-20

    Xinmei Zhang, Xingxing Wang, Wei Wang, Zhipeng Yuan, Jin Zhang Peng, J M Shi, Peng He, Yunfeng Chang

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  29. Disentangling Structural Patterns in Quaternary Germanides with Materials Informatics and Exploratory Synthesis: RE 4 IrInGe 4 ( RE = Y, Ce–Nd, Sm, Gd, Ho–Er) Series

    Inorganic Chemistry · 2026-03-31

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