BetaCitation Atlas

RESEARCH PAPER · Journal of the American Chemical Society · 2017

Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC

Anton O. Oliynyk, Lawrence Asamoah Adutwum, Brent W. Rudyk, Harshil Pisavadia, Sogol Lotfi, Viktor H. Hlukhyy, James J. Harynuk, Arthur Mar, Jakoah Brgoch

Read original paper ↗

See where this research leads.

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

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABCOriginalDiscovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches145Ternary Bismuthide SrPtBi2: Computation and Experiment in Synergism to Explore Solid-State Materials4Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons200Crystal Structure Prediction via Deep Learning355Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf−In System8Machine Learning Directed Search for Ultraincompressible, Superhard Materials317How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics316Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks14Hard and superhard materials: A computational perspective57A machine learning approach for predicting formation enthalpy: A case study of Mackay-type approximants of icosahedral quasicrystals2Machine learning for renewable energy materials312Machine Learning-Directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-Confined Regime63Solving the Coloring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation29Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning116Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures15Feature vector clustering molecular pairs in computer simulations2Recent advances and applications of machine learning in solid-state materials science2607Discovery of Highly Polymorphic Organic Materials: A New Machine Learning Approach1Machine Learning Optimization of p-Type Transparent Conducting Films42Inverse Design of Solid-State Materials via a Continuous Representation379Virtual Issue on Machine-Learning Discoveries in Materials Science29Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules124Navigating the design space of inorganic materials synthesis using statistical methods and machine learning43Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data29Design, Synthesis and Characterization of New Superconductors0Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches161Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis36General Protocol for the Accurate Prediction of Molecular 13C/1H NMR Chemical Shifts via Machine Learning Augmented DFT86Computer-Aided Design of Compounds with Crystal Structure of Melilites1A New Era of Inorganic Materials Discovery Powered by Data Science5Accelerating inverse crystal structure prediction by machine learning: A case study of carbon allotropes26Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning40AI Applications through the Whole Life Cycle of Material Discovery192Machine learning in experimental materials chemistry66Polymorphic Metastability in Colloidal Semiconductor Nanocrystals46Structure prediction of two-dimensional materials based on neural network-driven evolutionary technique4Clathrate XI K58Zn122Sb207: A New Branch on the Clathrate Family Tree12Clathrate XI K58Zn122Sb207: A New Branch on the Clathrate Family Tree8Machine learning in materials design: Algorithm and application*62Chalcophosphates: A Treasure House of Infrared Nonlinear Optical Materials75Training machine-learning potentials for crystal structure prediction using disordered structures43A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds58Atomic descriptors generated from coordination polyhedra in crystal structures6Synthesis and superconductivity of new TiNiSi-type equiatomic germanide ThIrGe5Materials discovery through machine learning formation energy78Predictions of Chalcospinels with Composition ABCX4 (X = S or Se)7Machine learning identified materials descriptors for ferroelectricity110An efficient and accurate numerical determination of the cluster resolution metric in two dimensions12Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning57Machine learning analysis of microwave dielectric properties for seven structure types: The role of the processing and composition19Machine Learning in Solid‐State Chemistry: Heusler Compounds0Coordination Polymers Constructed from Pyrogallol[4]arene-Assembled Metal–Organic Nanocapsules41Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry80Innovative Materials Science via Machine Learning194Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control68Search methods for inorganic materials crystal structure prediction27DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions0DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions4DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions3Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization77DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions28Machine-learning enabled thermodynamic model for the design of new rare-earth compounds35Hybrid DFT/Data-Driven Approach for Searching for New Quasicrystal Approximants in Sc-X (X = Rh, Pd, Ir, Pt) Systems9Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Data-driven materials discovery and synthesis using machine learning methods14Assessing comparable bioconcentration potentials for nanoparticles in aquatic organisms via combined utilization of machine learning and toxicokinetic models9Halide-driven polymorph selectivity in the synthesis of MnX (X = S, Se) nanoparticles7Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd36Synthesis and Crystal Structure of the Zintl Phases NaSrSb, NaBaSb and NaEuSb11Representations of Materials for Machine Learning133Data quantity governance for machine learning in materials science228Thermoelectric properties and Kondo transition in the pseudo‐gap metals TiNiSi and TiNiGe0A multiclass classification model for predicting the thermal conductivity of uranium compounds3Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells8Leveraging machine learning in porous media89Towards quantitative evaluation of crystal structure prediction performance11Low Lattice Thermal Conductivity-Driven Promising Thermoelectric Figure of Merit in NaSrSb and NaBaSb Zintl Phases11Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges31High-Temperature Oxidation-Resistance of Rare-Earth Transition Metal Silicides: Crystal Chemistry Insights from the Sc–Os–Si System2Compositional optimization of rare earth permanent magnetic alloy by machine learning2Machine learning prediction of materials properties from chemical composition: Status and prospects29Composition and structure analyzer/featurizer for explainable machine-learning models to predict solid state structures13Elemental Reactivity Maps for Materials Discovery1Recent 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 Validation7Artificial Intelligence‐Driven Approaches in Semiconductor Research44Operando Evaluation of NIR-Absorbing Squaraine Molecules for Dye-Sensitized Solar Cells: An Informatics-Driven Molecular Machine Learning Framework4Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control2Dataset of prototype structures adopted by intermetallic compounds with AB stacking4Enhanced Néel Temperature and Unusual Thermal Expansion in Flux-Grown FeCrAs Crystals1Machine 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) Series1Structure–Optical Property Relationships in AMM′Q 3 Chalcogenides0Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

95 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches

    Accounts of Chemical Research · 2017-12-15

    Anton O. Oliynyk, Arthur Mar

    Read original paper ↗ ·

  2. Ternary Bismuthide SrPtBi2: Computation and Experiment in Synergism to Explore Solid-State Materials

    The Journal of Physical Chemistry C · 2018-02-14

    Xin Gui, Xin Zhao, Zuzanna Sobczak, Cai‐Zhuang Wang, Tomasz Klimczuk, Kai-Ming Ho, Weiwei Xie

    Read original paper ↗ ·

  3. Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons

    Chemistry of Materials · 2018-05-19

    Jake Graser, Steven K. Kauwe, Taylor D. Sparks

    Read original paper ↗ ·

  4. Crystal Structure Prediction via Deep Learning

    Journal of the American Chemical Society · 2018-06-06

    Kevin Ryan, Jeff Lengyel, Michael Shatruk

    Read original paper ↗ ·

  5. Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf−In System

    Inorganic Chemistry · 2018-06-21

    Anton O. Oliynyk, Michael W. Gaultois, Martin Hermus, Andrew J. Morris, Arthur Mar, Jakoah Brgoch

    Read original paper ↗ ·

  6. Machine Learning Directed Search for Ultraincompressible, Superhard Materials

    Journal of the American Chemical Society · 2018-07-16

    Aria Mansouri Tehrani, Anton O. Oliynyk, Marcus E. Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D. Sparks, Jakoah Brgoch

    Read original paper ↗ ·

  7. How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics

    ACS Nano · 2018-07-20

    Bing Cao, Lawrence Asamoah Adutwum, Anton O. Oliynyk, Erik J. Luber, Brian C. Olsen, Arthur Mar, Jillian M. Buriak

    Read original paper ↗ ·

  8. Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

    Integrating materials and manufacturing innovation · 2018-08-27

    Dennis M. Dimiduk, Elizabeth A. Holm, Stephen R. Niezgoda

    Read original paper ↗ ·

  9. CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks

    arXiv (Cornell University) · 2018-10-26

    Asma Nouira, Nataliya Sokolovska, Jean‐Claude Crivello

    Read original paper ↗ ·

  10. Hard and superhard materials: A computational perspective

    Journal of Solid State Chemistry · 2018-11-22

    Aria Mansouri Tehrani, Jakoah Brgoch

    Read original paper ↗ ·

  11. A machine learning approach for predicting formation enthalpy: A case study of Mackay-type approximants of icosahedral quasicrystals

    AIP conference proceedings · 2019-01-01

    Roman A. Eremin, Pavel N. Zolotarev, Tilmann Leisegang, Pavlo Solokha

    Read original paper ↗ ·

  12. Machine learning for renewable energy materials

    Journal of Materials Chemistry A · 2019-01-01

    Geun Ho Gu, Juhwan Noh, I. P. Kim, Yousung Jung

    Read original paper ↗ ·

  13. Machine Learning-Directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-Confined Regime

    Chemistry of Materials · 2019-04-11

    Erick J. Braham, Junsang Cho, Kristel M. Forlano, David F. Watson, Raymundo Arróyave, Sarbajit Banerjee

    Read original paper ↗ ·

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

    Inorganic Chemistry · 2019-06-25

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

    Read original paper ↗ ·

  15. Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning

    Small Methods · 2019-06-26

    Shuaihua Lu, Qionghua Zhou, Liang Ma, Yilv Guo, Jinlan Wang

    Read original paper ↗ ·

  16. Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures

    Inorganic Chemistry · 2019-07-03

    Gayatri Viswanathan, Anton O. Oliynyk, Erin Antono, Julia Ling, Bryce Meredig, Jakoah Brgoch

    Read original paper ↗ ·

  17. Feature vector clustering molecular pairs in computer simulations

    Journal of Computational Chemistry · 2019-07-16

    Hanwen Pei, Aatto Laaksonen

    Read original paper ↗ ·

  18. Recent advances and applications of machine learning in solid-state materials science

    npj Computational Materials · 2019-08-08

    Jonathan Schmidt, Mário R. G. Marques, Silvana Botti, Miguel A. L. Marques

    Read original paper ↗ ·

  19. Discovery of Highly Polymorphic Organic Materials: A New Machine Learning Approach

    ChemRxiv · 2019-08-13

    Zied Hosni, Annalisa Riccardi, Stephanie Yerdelen, Alan R. G. Martin, Deborah Bowering, Alastair James Florence

    Read original paper ↗ ·

  20. Machine Learning Optimization of p-Type Transparent Conducting Films

    Chemistry of Materials · 2019-08-14

    Lingfei Wei, Xiaojie Xu, Gurudayal Gurudayal, James Bullock, Joel W. Ager

    Read original paper ↗ ·

  21. Inverse Design of Solid-State Materials via a Continuous Representation

    Matter · 2019-10-02

    Juhwan Noh, Jaehoon Kim, Helge Sören Stein, Benjamín Sánchez-Lengeling, John M. Gregoire, Alán Aspuru‐Guzik, Yousung Jung

    Read original paper ↗ ·

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

    Chemistry of Materials · 2019-10-22

    Anton O. Oliynyk, Jillian M. Buriak

    Read original paper ↗ ·

  23. Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules

    Journal of the American Chemical Society · 2019-12-24

    Yunchao Xie, Chen Zhang, Xiangquan Hu, Chi Zhang, Steven P. Kelley, Jerry L. Atwood, Jian Lin

    Read original paper ↗ ·

  24. Navigating the design space of inorganic materials synthesis using statistical methods and machine learning

    Dalton Transactions · 2020-01-01

    Erick J. Braham, Rachel D. Davidson, Mohammed Al‐Hashimi, Raymundo Arróyave, Sarbajit Banerjee

    Read original paper ↗ ·

  25. Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data

    Batteries & Supercaps · 2020-02-03

    Natalia V. Kireeva, Vladislav S. Pervov

    Read original paper ↗ ·

  26. Design, Synthesis and Characterization of New Superconductors

    Unknown journal · 2020-04-01

    Xin Gui

    Read original paper ↗ ·

  27. Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches

    Annual Review of Materials Research · 2020-05-18

    James E. Saal, Anton O. Oliynyk, Bryce Meredig

    Read original paper ↗ ·

  28. Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis

    Matter · 2020-06-05

    Sogol Lotfi, Ziyan Zhang, Gayatri Viswanathan, Kaitlyn Fortenberry, Aria Mansouri Tehrani, Jakoah Brgoch

    Read original paper ↗ ·

  29. General Protocol for the Accurate Prediction of Molecular 13C/1H NMR Chemical Shifts via Machine Learning Augmented DFT

    Journal of Chemical Information and Modeling · 2020-06-30

    Peng Gao, Jun Zhang, Jun Zhang, Qian Peng, Jie Zhang, Jie Zhang, Vassiliki‐Alexandra Glezakou

    Read original paper ↗ ·

  30. Computer-Aided Design of Compounds with Crystal Structure of Melilites

    Inorganic Materials Applied Research · 2020-07-01

    N. N. Kiselyova, Victor A. Dudarev, V. V. Ryazanov, Oleg Valentinovich Sen'ko, A. A. Dokukin

    Read original paper ↗ ·

  31. A New Era of Inorganic Materials Discovery Powered by Data Science

    Royal Society of Chemistry eBooks · 2020-07-15

    Ya Zhuo, Aria Mansouri Tehrani, Jakoah Brgoch

    Read original paper ↗ ·

  32. Accelerating inverse crystal structure prediction by machine learning: A case study of carbon allotropes

    Frontiers of Physics · 2020-07-15

    Wen Tong, Qun Wei, Haiyan Yan, Meiguang Zhang, Xuanmin Zhu

    Read original paper ↗ ·

  33. Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning

    Integrating materials and manufacturing innovation · 2020-07-24

    Steven K. Kauwe, Taylor Welker, Taylor D. Sparks

    Read original paper ↗ ·

  34. AI Applications through the Whole Life Cycle of Material Discovery

    Matter · 2020-08-01

    Jiali Li, Kaizhuo Lim, Haitao Yang, Zekun Ren, S. Raghavan, Po‐Yen Chen, Tonio Buonassisi, Xiaonan Wang

    Read original paper ↗ ·

  35. Machine learning in experimental materials chemistry

    Catalysis Today · 2020-08-21

    Balaranjan Selvaratnam, Ranjit T. Koodali

    Read original paper ↗ ·

  36. Polymorphic Metastability in Colloidal Semiconductor Nanocrystals

    ChemNanoMat · 2020-08-25

    Bryce A. Tappan, Richard L. Brutchey

    Read original paper ↗ ·

  37. Structure prediction of two-dimensional materials based on neural network-driven evolutionary technique

    Computational Materials Science · 2020-09-10

    K. Zberecki

    Read original paper ↗ ·

  38. Clathrate XI K58Zn122Sb207: A New Branch on the Clathrate Family Tree

    Angewandte Chemie International Edition · 2020-09-16

    Tori Cox, Volodymyr Gvozdetskyi, Mark Bertolami, Shannon Lee, Kristian Shipley, Oleg I. Lebedev, Julia V. Zaikina

    Read original paper ↗ ·

  39. Clathrate XI K58Zn122Sb207: A New Branch on the Clathrate Family Tree

    Angewandte Chemie · 2020-09-16

    Tori Cox, Volodymyr Gvozdetskyi, Mark Bertolami, Shannon Lee, Kristian Shipley, Oleg I. Lebedev, Julia V. Zaikina

    Read original paper ↗ ·

  40. Machine learning in materials design: Algorithm and application*

    Chinese Physics B · 2020-10-14

    Zhilong Song, Xiwen Chen, Fanbin Meng, Guanjian Cheng, Chen Wang, Zhongti Sun, Wan‐Jian Yin

    Read original paper ↗ ·

  41. Chalcophosphates: A Treasure House of Infrared Nonlinear Optical Materials

    Crystal Growth & Design · 2020-10-23

    Zhuang Li, Jiyong Yao, Yicheng Wu

    Read original paper ↗ ·

  42. Training machine-learning potentials for crystal structure prediction using disordered structures

    Physical review. B./Physical review. B · 2020-12-18

    Chang‐Ho Hong, Jeong Min Choi, Wonseok Jeong, Sungwoo Kang, Suyeon Ju, Kyeongpung Lee, Jisu Jung, Yong Youn, Seungwu Han

    Read original paper ↗ ·

  43. A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds

    Inorganic Chemistry Frontiers · 2021-01-01

    Jin-Woong Lee, Woon Bae Park, Minseuk Kim, Satendra Pal Singh, Myoungho Pyo, Kee‐Sun Sohn

    Read original paper ↗ ·

  44. Atomic descriptors generated from coordination polyhedra in crystal structures

    Science and Technology of Advanced Materials Methods · 2021-01-01

    Yuki Inada, Yukari Katsura, Masaya Kumagai, Kaoru Kimura

    Read original paper ↗ ·

  45. Synthesis and superconductivity of new TiNiSi-type equiatomic germanide ThIrGe

    Materials Advances · 2021-01-01

    Guorui Xiao, Qinqing Zhu, Yanwei Cui, Bin Liu, Jifeng Wu, Bai-Zhuo Li, Siqi Wu, Guang‐Han Cao, Zhi Ren

    Read original paper ↗ ·

  46. Materials discovery through machine learning formation energy

    Journal of Physics Energy · 2021-02-09

    Gordon G. C. Peterson, Jakoah Brgoch

    Read original paper ↗ ·

  47. Predictions of Chalcospinels with Composition ABCX4 (X = S or Se)

    Inorganic Materials Applied Research · 2021-03-01

    N. N. Kiselyova, Victor A. Dudarev, V. V. Ryazanov, Oleg Valentinovich Sen'ko, A. A. Dokukin

    Read original paper ↗ ·

  48. Machine learning identified materials descriptors for ferroelectricity

    Acta Materialia · 2021-03-20

    Jingjin He, Junjie Li, Chuanbao Liu, Changxin Wang, Yan Zhang, Cheng Wen, Dezhen Xue, Jiangli Cao, Yanjing Su, Lijie J. Qiao, Yang Bai

    Read original paper ↗ ·

  49. An efficient and accurate numerical determination of the cluster resolution metric in two dimensions

    Journal of Chemometrics · 2021-04-17

    Michael Armstrong, A. Paulina de la Mata, James J. Harynuk

    Read original paper ↗ ·

  50. Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning

    International Journal of Extreme Manufacturing · 2021-05-10

    Yun Chen, Yanhui Chen, Junyu Long, Dachuang Shi, Xin Chen, Maoxiang Hou, Jian Gao, Huilong Liu, Yunbo He, Bi Fan, Ching‐Ping Wong, Ni Zhao

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.