New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

Cited by (243)

Items 101–200 of 243

1. Machine learning promotes the development of all-solid-state batteries
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2. Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow
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3. Important Descriptors and Descriptor Groups of Curie Temperatures of Rare-earth Transition-metal Binary Alloys
   Hieu‐Chi Dam, Viet-Cuong Nguyen, Tien-Lam Pham, Anh Tuan Nguyen, Kiyoyuki Terakura, Takashi Miyake, Hiori Kino · 2018 · Journal of the Physical Society of Japan
4. Coupling in situ experiments and modeling – Opportunities for data fusion, machine learning, and discovery of emergent behavior
   Michael D. Sangid · 2019 · Current Opinion in Solid State and Materials Science
5. Machine learning-enabled prediction of chemical durability of A2B2O7 pyrochlore and fluorite
   Bowen Gong, Kun Yang, James A. Lian, Jianwei Wang · 2021 · Computational Materials Science
6. Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning
   Amanda Susan Barnard, George Opletal · 2019 · Nanoscale
7. Orientation-dependent properties of epitaxially strained perovskite oxide thin films: Insights from first-principles calculations
   Thomas Angsten, Lane W. Martin, Mark Asta · 2017 · Physical review. B./Physical review. B
8. Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations
   Desmond Edem Primus Klenam, Tabiri Kwayie Asumadu, Mobin Vandadi, Nima Rahbar, Fred McBagonluri, Winston Oluwole Soboyejo · 2023 · Results in Materials
9. Visualising multi-dimensional structure/property relationships with machine learning
   Baichuan Sun, Amanda Susan Barnard · 2019 · Journal of Physics Materials
10. Discovery of superionic conductors by ensemble-scope descriptor
    Seiji Kajita, Nobuko Ohba, Akitoshi Suzumura, Shin Tajima, Ryoji Asahi · 2020 · NPG Asia Materials
11. Machine learning properties of binary wurtzite superlattices
    Ghanshyam Pilania, Xiang-Yang Liu · 2018 · Journal of Materials Science
12. Machine learning-based performance predictions for steels considering manufacturing process parameters: a review
    Wei Fang, Jiaxin Huang, Tiexu Peng, Yang Long, Fuxing Yin · 2024 · Journal of Iron and Steel Research International
13. Predicting Perovskite Performance with Multiple Machine-Learning Algorithms
    Ruoyu Li, Qin Deng, Dong Tian, Daoye Zhu, Bin Lin · 2021 · Crystals
14. Machine learned synthesizability predictions aided by density functional theory
    Andrew S. Lee, Suchismita Sarker, James E. Saal, Logan T. Ward, Christopher K. H. Borg, Apurva Mehta, Christopher Wolverton · 2022 · Communications Materials
15. Machine learning prediction of materials properties from chemical composition: Status and prospects
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16. Using artificial intelligence to accelerate materials development
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17. Gradient boosted and statistical feature selection workflow for materials property predictions
    Son Gyo Jung, Guwon Jung, Jacqueline M. Cole · 2023 · The Journal of Chemical Physics
18. Integrating artificial intelligence with piezoelectric nanogenerators: a review on advancements in smart energy harvesting technologies
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19. Text mining assisted review of the literature on Li-O2 batteries
    Amangeldi Torayev, Pieter C. M. M. Magusin, Clare P. Grey, Céline Merlet, Alejandro A. Franco · 2019 · Journal of Physics Materials
20. Descriptors for dielectric constants of perovskite-type oxides by materials informatics with first-principles density functional theory
    Yusuke Noda, Masanari Otake, Masanobu Nakayama · 2020 · Science and Technology of Advanced Materials
21. Representing molecular and materials data for unsupervised machine learning
    Ellen T. Swann, Baichuan Sun, Deidre M. Cleland, Amanda Susan Barnard · 2018 · Molecular Simulation
22. Seeking for Low Thermal Conductivity Atomic Configurations in SiGe Alloys with Bayesian Optimization
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23. The future of bone regeneration: Artificial intelligence in biomaterials discovery
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24. Machine learning-assisted design of high-performance perovskite photodetectors: a review
    Xiaohui Fiona Li, Yongxiang Mai, Chunfeng Lan, Fu Yang, Putao Zhang, Shengjun Li · 2024 · Advanced Composites and Hybrid Materials
25. Hochdimensionale neuronale Netze für Potentialhyperflächen großer molekularer und kondensierter Systeme
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26. Convergence and pitfalls of density functional perturbation theory phonons calculations from a high-throughput perspective
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27. High-throughput assessment of hypothetical zeolite materials for their synthesizeability and industrial deployability
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28. Applying Bayesian Approach to Combinatorial Problem in Chemistry
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29. Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts
    Vijay Kumar Choyal, N. Reddy Kala Sagar, Gopalakrishnan Sai Gautam · 2024 · Journal of Chemical Theory and Computation
30. Frontiers in strain-engineered multifunctional ferroic materials
    Joshua Agar, Shishir A. Pandya, Ruijuan Xu, Ajay K. Yadav, Zhiqi Liu, Thomas Angsten, Sahar Saremi, Mark Asta, R. Ramesh, Lane W. Martin · 2016 · MRS Communications
31. Active Learning for Adsorption Simulations: Evaluation, Criteria Analysis, and Recommendations for Metal–Organic Frameworks
    Etinosa Osaro, Krishnendu Mukherjee, Yamil J. Colón · 2023 · Industrial & Engineering Chemistry Research
32. Research on college English teaching based on data mining technology
    Jinhui Duan, Rui Gao · 2021 · EURASIP Journal on Wireless Communications and Networking
33. Machine Learning for Designing Mixed Metal Halides for Efficient Ammonia Separation and Storage
    Armando de Rezende, Mahdi Malmali, Pavlo O. Dral, Hans Lischka, Daniel Tunega, Adélia J. A. Aquino · 2022 · The Journal of Physical Chemistry C
34. Machine learning-driven design of promising perovskites for photovoltaic applications: A review
    Jinlian Chen, Mengjia Feng, Chenyang Zha, Cairu Shao, Linghai Zhang, Lin Wang · 2022 · Surfaces and Interfaces
35. Materials genome: research progress, challenges and outlook
    Yunqi Li, Lunyang Liu, Wenduo Chen, Lijia An · 2017 · Scientia Sinica Chimica
36. Classifying and predicting the electron affinity of diamond nanoparticles using machine learning
    Christopher A. Feigl, Benyamin Motevalli, Amanda J. Parker, Baichuan Sun, Amanda Susan Barnard · 2019 · Nanoscale Horizons
37. Machine learning for a sustainable energy future
    Burcu Oral, Ahmet Çoşgun, Aysegul Kilic, Damla Eroğlu, Mehmet Erdem Günay, Ramazan Yıldırım · 2024 · Chemical Communications
38. Research on Cu-Sn machine learning interatomic potential with active learning strategy
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39. Current state of the art and future perspectives for photocatalytic CO2 reduction
    Pınar Özdemir, Ramazan Yıldırım · 2026 · Journal of Energy Chemistry
40. Complex amorphous oxides: property prediction from high throughput DFT and AI for new material search
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41. ML@ChemE: Past, Present, and Future of Machine Learning in Chemical Engineering
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42. Machine‐Learning Accelerating the Development of Perovskite Photovoltaics
    Tiantian Liu, Sen Wang, Yinguang Shi, Lei Wu, Ruiyu Zhu, Yong Wang, Jun Zhou, Wallace C. H. Choy · 2023 · Solar RRL
43. Uncovering the Dynamics in the Application of Machine learning in Computational Finance: A Bibliometric and Social Network Analysis
    Samuel-Soma M. Ajibade, Muhammed Basheer Jasser, David Olayemi Alebiosu, Ismail Ahmed Al-Qasem Al-Hadi, Ghassan Saleh ALDharhani, Farrukh Hassan, Bright Akwasi Gyamfi · 2024 · International Journal of Economics and Financial Issues
44. Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators
    Ghanshyam Pilania, Xiang-Yang Liu, ‪Zhehui Wang · 2019 · Journal of Materials Science
45. Machine learning enables robust prediction of thermal boundary conductance of 2D substrate interfaces
    Cameron J. Foss, Zlatan Akšamija · 2023 · Applied Physics Letters
46. Texture based image classification for nanoparticle surface characterisation and machine learning
    Baichuan Sun, Amanda Susan Barnard · 2018 · Journal of Physics Materials
47. Sputter-Deposited Mo Thin Films: Multimodal Characterization of Structure, Surface Morphology, Density, Residual Stress, Electrical Resistivity, and Mechanical Response
    Matias Kalaswad, Joyce Custer, Sadhvikas Addamane, Ryan M. Khan, Luis Jauregui, Tomas F. Babuska, Amelia A. Henriksen, Frank W. DelRio‬, Rémi Dingreville, Brad L. Boyce et al. · 2023 · Integrating materials and manufacturing innovation
48. Process-Function Data Mining for the Discovery of Solid-State Iron-Oxide PV
    Elana Borvick, Assaf Y. Anderson, Hannah‐Noa Barad, Maayan Priel, David A. Keller, Adam Ginsburg, Kevin J. Rietwyk, Simcha Meir, Arie Zaban · 2017 · ACS Combinatorial Science
49. Artificial intelligence in the development of structural and functional materials: A transformative frontier and complementary enabler of the rational alloy design framework
    Desmond Edem Primus Klenam · 2025 · Next research.
50. Machine learning for gas–solid interaction materials and devices
    Peiyuan Liu, Xiaoyang Zhu, Xu Ran, Hengchang Bi, Xiao Bing Huang, Ning Gu · 2024 · Coordination Chemistry Reviews
51. MaTableGPT: GPT‐Based Table Data Extractor from Materials Science Literature
    Gyeong Hoon Yi, Jiwoo Choi, Hyeongyun Song, Olivia Miano, Jaewoong Choi, Kihoon Bang, Byungju Lee, Seok Su Sohn, David J. Buttler, Anna M. Hiszpanski et al. · 2025 · Advanced Science
52. Data-Based Methods for Materials Design and Discovery
    Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman · 2020 · Synthesis lectures on materials and optics
53. Enhanced Hyperbox Classifier Model for Nanomaterial Discovery
    Jose Isagani B. Janairo, Kathleen B. Aviso, Michael Angelo B. Promentilla, Raymond R. Tan · 2020 · AI
54. Data Mining for Predicting Customer Satisfaction Using Clustering Techniques
    Kartika Purwandari, Join W. C. Sigalingging, Muhammad Fhadli, Shinta Nur Arizky, Bens Pardamean · 2020 · International Conference on Information Management and Technology (ICIMTech)
55. Testing whether flat bands in the calculated electronic density of states are good predictors of superconducting materials
    Zoë S. Yang, Austin M. Ferrenti, R. J. Cava · 2020 · Journal of Physics and Chemistry of Solids
56. Efficient sampling for decision making in materials discovery*
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57. Statistics on magnetic properties of Co compounds: A database-driven method for discovering Co-based ferromagnets
    Journey K. Byland, Yunshu Shi, David Parker, Jing‐Tai Zhao, S.Q. Ding, Rogelio Mata, Haley E. Magliari, Andriy M. Palasyuk, Sergey L. Bud’ko, Paul C. Canfield et al. · 2022 · Physical Review Materials
58. Materials Informatics
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59. Potential of Machine Learning Algorithms in Material Science: Predictions in Design, Properties, and Applications of Novel Functional Materials
    Purvi Bhatt, Neha Singh, Sumit Chaudhary · 2023 · Springer eBooks
60. A materials informatics driven fine-tuning of triazine-based electron-transport layer for organic light-emitting devices
    Kosuke Sato, Kazuki Hattori, Fuminari Uehara, Tomoko Kitaguni, Toshiki Nishiura, Takuya Yamagata, Keisuke Nomura, Naoki Matsumoto, Tsuyoshi Tanaka, Hidenori Aihara · 2024 · Scientific Reports
61. Machine Learning-based estimation and explainable artificial intelligence-supported interpretation of the critical temperature from magnetic ab initio Heusler alloys data
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62. Machine Learning for Catalytic Reaction Systems: A Framework for Complex Chemical Processes
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63. Methodological framework for materials discovery using machine learning
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64. APPLICATION ANALYSIS OF DIGITAL NEURAL NETWORK-BASED DATA MINING METHOD IN MAXIMIZING THE PERFORMANCE OF SPORTS TRAINING
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65. Machine learning modeling for the prediction of materials energy
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66. Superconductor Discovery in the Emerging Paradigm of Materials Informatics
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67. Systematic searches for new inorganic materials assisted by materials informatics
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68. High-Accuracy Machine-Learned Interatomic Potentials for the Phase Change Material Ge3Sb6Te5
    Wei Yu, Zhaofu Zhang, Xuhao Wan, Jinhao Su, Qingzhong Gui, Hailing Guo, Hongxia Zhong, John Robertson, Yuzheng Guo · 2023 · Chemistry of Materials
69. Networks of materials: Construction and structural analysis
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70. Data-Driven Design of Classes of Ruthenium Nanoparticles Using Multitarget Bayesian Inference
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71. Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem
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72. Using experimental data in computationally guided rational design of inorganic materials with machine learning
    Heather J. Kulik · 2025 · Journal of materials research/Pratt's guide to venture capital sources
73. Identifying hidden high-dimensional structure/property relationships using self-organizing maps
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74. Machine learning of organic solvents reveals an extraordinary axis in Hansen space as indicator of spherical precipitation of polymers
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75. Using machine learning to guide the synthesis of supported palladium catalysts with desired palladium dispersion
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76. Prediction of Hardness and Fracture Toughness in High-Entropy Alloy Bonded Cemented Carbides through Machine Learning
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77. Machine Learning-Driven Density Prediction for Nanomaterials
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78. Materials Informatics and Data System for Polymer Nanocomposites Analysis and Design
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79. Featureless approach for predicting Critical Temperature of Superconductors
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80. Nonstationarity Analysis of Materials Microstructures via Fisher Score Vectors
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81. Causal Paths Allowing Simultaneous Control of Multiple Nanoparticle Properties Using Multi‐Target Bayesian Inference
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82. Learnable features for predicting properties of metal-organic frameworks with deep neural networks
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83. Machine Learning–Based Analysis of Sustainable Biochar Production Processes
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84. Feature Engineering of Solid‐State Crystalline Lattices for Machine Learning
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85. Electrochemical Performance of Carbon derived from Bamboo Shoot as Anode Materials for Lithium-ion Battery
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86. Predicting Drug Interactions to Unassociated Biomedical Implants Using Machine Learning Techniques and Model Polymers
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87. High throughput characterization method of electrical and phonon properties by dielectric resonant spectroscopy
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88. Energy-GNoME: A living database of selected materials for energy applications
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89. Physics‐Guided Data‐Mining Driven Design of Room‐Temperature Multiferroic Perovskite Oxides
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90. Materials Informatics Approach to the Identification of One-Band Correlated Materials Analogous to the Cuprates
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91. Big data for smart factories: A bibliometric analysis
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92. A framework for supervised and unsupervised segmentation and classification of materials microstructure images
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93. Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science
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94. Data-driven crack assessment based on surface measurements
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95. Predicting the mechanical properties of unidirectional fiber reinforced plastics from fiber and resin properties using data mining
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96. Machine learning workflow for microparticle composite thin-film process–structure linkages
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97. Materials Design in Digital Era: Challenges and Opportunities
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98. Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning
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99. Evolution of MG AZ31 twin activation with strain: A machine learning study
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100. Causal emergent principles and relations for mechanical properties of covalent and ionic crystals
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