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RESEARCH PAPER · International Journal of Hydrogen Energy · 2022

Predicting hydrogen storage capacity of V–Ti–Cr–Fe alloy via ensemble machine learning

Ziliang Lu, Jianwei Wang, Yuanfang Wu, Xiumei Guo, Wei Xiao

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Predicting hydrogen storage capacity of V–Ti–Cr–Fe alloy via ensemble machine learningOriginalHigh pressure solidification of alloying substitution and promotion of hydrogen storage properties in AB2-type Y-Zr-Ti-Fe based alloys5Prediction of hydrogen concentration responsible for hydrogen-induced mechanical failure in martensitic high-strength steels19Predictiing the Stacking Faulting Energy in Fcc High-Entropy Alloys Based on Data-Driven Machine Learning0Simple local environment descriptors for accurate prediction of hydrogen absorption and migration in metal alloys17An Overview of the Recent Advances in Composite Materials and Artificial Intelligence for Hydrogen Storage Vessels Design145Effect of V content on hydrogen storage properties and cyclic durability of V–Ti–Cr–Fe alloys55Development of Fe-containing BCC hydrogen storage alloys with high vanadium concentration33Exploration and design of Mg alloys for hydrogen storage with supervised machine learning73Data-driven machine learning for alloy research: Recent applications and prospects21Prediction and theoretical investigation of dehydrogenation enthalpy of V–Ti–Cr–Fe alloy using machine learning and density functional theory18High-entropy alloys for solid hydrogen storage: a review169Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning45Machine learning enabled customization of performance-oriented hydrogen storage materials for fuel cell systems72Hydrogen storage behaviours of high entropy alloys: A Review113Solid state hydrogen storage: Decoding the path through machine learning48Properties of Ti-Based Hydrogen Storage Alloy13APPLICATION OF HIGH-ENTROPY ALLOYS IN HYDROGEN STORAGE TECHNOLOGY9Enhanced hydrogen storage efficiency with sorbents and machine learning: a review95Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data96Research progress and development tendency on storage mechanism of multi‐principal element alloys for hydrogen/tritium storage38Vanadium‐based alloy for hydrogen storage: a review74AI-driven development of high-performance solid-state hydrogen storage42High entropy alloys for hydrogen storage applications: A machine learning-based approach35Construction of hybrid models based on cascade technique using basic machine learning models: An application as photocurrent density predictor of the photoelectrode in PEC cell7The integral role of high‐entropy alloys in advancing solid‐state hydrogen storage63Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives92MH-PCTpro: A machine learning model for rapid prediction of pressure-composition-temperature (PCT) isotherms17Predicting the hydrogen storage capacity of alumina pillared interlayer clays using interpretable ensemble machine learning6Room temperature hydrogen storage properties of Ti-Zr-Mn-Fe-Co high-entropy alloys designed by semi-empirical descriptors, thermodynamic calculations and machine learning35Machine learning-driven prediction of absorption time breakthrough in helical hydride hydrogen storage canisters: Insights for lightweight assemblies4Machine learning-driven alloy digital design for hydrogen storage: a review12Role of compositionally complex and high entropy alloys for hydrogen storage: A bibliometric and mechanistic assessment16A tool to conjecture hydrogen storage capacity of high entropy alloys9Research progress in solid-state hydrogen storage alloys: A review120Discovering elemental effects on hydrogen storage in TiMn2-type alloys using explainable machine learning12The enthalpy changes for hydrogenation/dehydrogenation of Mg-based alloys29Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications11Solid-state hydrogen storage alloys for production-storage and transportation-application coupling at ambient temperature: A review53FIND: a forward–inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven machine learning13Hydrogen bonding system in metal alloys0Advancing safety of hydrogen polymeric tanks: A review of permeation mitigation, leak detection, and smart monitoring14Smart screening of hydrogen storage capacities in MOFs using a tailored machine learning6Amorphous High-Entropy Alloys as Innovative Materials for Hydrogen Energy: State of the Art and Development Prospects2Leveraging compositional descriptors via explainable machine learning to understand hydrogen storage behavior4Introduction and Research Trends in Hydrogen Storage Alloys for Solid-state Hydrogen Storage0A workflow-centric review of artificial intelligence in solid-state hydrogen storage: toward trustworthy AI for materials design0Explainable ensemble learning reveals site-driven d z 2 orbital occupancy tuning for enhanced hydrogen evolution on metal-doped Ni-loaded BN catalysts0Integrated learning-assisted design of metal–nitrogen–carbon single-atom catalysts: electronegativity regulates the coupling rules of interfacial valence electrons4Interpretable Ensemble Learning Reveals the d z 2 -Orbital-Regulated H Adsorption Mechanism of Metal-Doped Ni/BN Catalysts0Breaking small sample data barriers: Machine learning prediction of hydrogen storage capacity in Mg-based alloys via data augmentation0Toward intelligent design of solid-state hydrogen storage: trends, challenges, and machine learning insights6Digital Hydrogen-S: An open-access interactive Data platform for solid-state hydrogen storage materials9Artificial intelligence-driven discovery and design of solid-state hydrogen storage materials8Design and performance modulation of V-based hydrogen storage alloys: a review2Interpretable machine learning framework for hydrogen storage capacity prediction in Ti-Zr-Mn-Cr-V high-entropy alloys: Influenced by valence electron average2Enhancing multi-component alloy composition prediction based on generative adversarial networks and proximal policy optimization2Multiscale computational design of hydrogen-compatible integrated structural-functional alloys5A focused mini-review on high-entropy intermetallics for hydrogen storage1High-Entropy Alloys as Materials for Solid-State Hydrogen Storage: From Fundamental Principles to Directed Design Strategies6Dual‐Metal Co‐Doping Strategy Enhances the Hydrogen Dissociation and Hydrogen Release Performance of LiBH 4 Assisted by Ensemble Learning0Prediction of new La-based metal hydride composition for hydrogen storage application via supervised machine learning approach0Thermodynamic modification of Mg-based hydrogen storage alloys: Strategies, mechanisms, and prospects0Stacking Ensemble Learning with Genetic Algorithm Optimization for Multi-Property Prediction and Inverse Design of BCC-Type V-Based Hydrogen Storage Alloys1Data-driven compositional optimization of Ti–V-based BCC alloys for room-temperature high-capacity hydrogen storage1Accelerating the discovery of high-capacity solid-state hydrogen storage materials: A predictive machine learning framework0High-entropy alloys for solid state hydrogen storage: Composition, electronic structure and thermodynamic-kinetic tailoring0

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  1. High pressure solidification of alloying substitution and promotion of hydrogen storage properties in AB2-type Y-Zr-Ti-Fe based alloys

    Journal of Alloys and Compounds · 2022-11-11

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  2. Prediction of hydrogen concentration responsible for hydrogen-induced mechanical failure in martensitic high-strength steels

    International Journal of Hydrogen Energy · 2022-11-30

    Eric A. K. Fangnon, Evgenii Malitckii, Renata Latypova, Pedro Vilaça

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  3. Predictiing the Stacking Faulting Energy in Fcc High-Entropy Alloys Based on Data-Driven Machine Learning

    SSRN Electronic Journal · 2023-01-01

    Xiaoyang Zhang, Ruifeng Dong, Qingwei Guo, Hua Hou, Yuhong Zhao

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  4. Simple local environment descriptors for accurate prediction of hydrogen absorption and migration in metal alloys

    Journal of Materials Chemistry A · 2023-01-01

    Vladislav O. Korostelev, James Wagner, Konstantin Klyukin

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  5. An Overview of the Recent Advances in Composite Materials and Artificial Intelligence for Hydrogen Storage Vessels Design

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    Mourad Nachtane, Mostapha Tarfaoui, Mohamed amine Abichou, Alexandre A. Vetcher, Marwane Rouway, Abdeouhaed Aâmir, Habib Mouadili, Houda Laaouidi, Hassan Naanani

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  6. Effect of V content on hydrogen storage properties and cyclic durability of V–Ti–Cr–Fe alloys

    International Journal of Hydrogen Energy · 2023-04-11

    Chao Yang, Qian Wang, Mingxing Wang, Yao Wang, Rui Du, Jichuan Dai, Yigang Yan, Yungui Chen, Chaoling Wu

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  7. Development of Fe-containing BCC hydrogen storage alloys with high vanadium concentration

    Journal of Alloys and Compounds · 2023-04-26

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  8. Exploration and design of Mg alloys for hydrogen storage with supervised machine learning

    International Journal of Hydrogen Energy · 2023-06-29

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  9. Data-driven machine learning for alloy research: Recent applications and prospects

    Materials Today Communications · 2023-07-19

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  10. Prediction and theoretical investigation of dehydrogenation enthalpy of V–Ti–Cr–Fe alloy using machine learning and density functional theory

    International Journal of Hydrogen Energy · 2023-07-28

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  11. High-entropy alloys for solid hydrogen storage: a review

    International Journal of Hydrogen Energy · 2023-08-08

    Long Luo, Liangpan Chen, Lirong Li, Lirong Li, Yiming Li, Yiming Li, Yiming Li, Linfeng Li, Chuanfei Li, Yongzhi Li, Yongzhi Li, Junjie Cui, Yongzhi Li, Yongzhi Li

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  12. Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning

    Journal of Materials Research and Technology · 2023-08-23

    Xiaoyang Zhang, Ruifeng Dong, Qingwei Guo, Hua Hou, Yuhong Zhao

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  13. Machine learning enabled customization of performance-oriented hydrogen storage materials for fuel cell systems

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    Panpan Zhou, Xuezhang Xiao, Xinyu Zhu, Yongpeng Chen, Yongpeng Chen, Weiming Lü, Mingyuan Piao, Ziming Cao, Miao Lu, Fang Fang, Zhinian Li, Lijun Jiang, Lixin Chen

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  14. Hydrogen storage behaviours of high entropy alloys: A Review

    Journal of Energy Storage · 2023-09-26

    Thabang Ronny Somo, Mykhaylo V. Lototskyy, Volodymyr A. Yartys, Moegamat Wafeeq Davids, Serge Nyallang Nyamsi

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  15. Solid state hydrogen storage: Decoding the path through machine learning

    International Journal of Hydrogen Energy · 2023-11-06

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  16. Properties of Ti-Based Hydrogen Storage Alloy

    Journal of Power and Energy Engineering · 2024-01-01

    Rui Xu, Tao Cheng, Chaoyu Li, Xue Yang, Junfeng Rong

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  17. APPLICATION OF HIGH-ENTROPY ALLOYS IN HYDROGEN STORAGE TECHNOLOGY

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  18. Enhanced hydrogen storage efficiency with sorbents and machine learning: a review

    Environmental Chemistry Letters · 2024-05-16

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  19. Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data

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  20. Research progress and development tendency on storage mechanism of multi‐principal element alloys for hydrogen/tritium storage

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  21. Vanadium‐based alloy for hydrogen storage: a review

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  22. AI-driven development of high-performance solid-state hydrogen storage

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  23. High entropy alloys for hydrogen storage applications: A machine learning-based approach

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  24. Construction of hybrid models based on cascade technique using basic machine learning models: An application as photocurrent density predictor of the photoelectrode in PEC cell

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  25. The integral role of high‐entropy alloys in advancing solid‐state hydrogen storage

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  27. MH-PCTpro: A machine learning model for rapid prediction of pressure-composition-temperature (PCT) isotherms

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  28. Predicting the hydrogen storage capacity of alumina pillared interlayer clays using interpretable ensemble machine learning

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  29. Room temperature hydrogen storage properties of Ti-Zr-Mn-Fe-Co high-entropy alloys designed by semi-empirical descriptors, thermodynamic calculations and machine learning

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  30. Machine learning-driven prediction of absorption time breakthrough in helical hydride hydrogen storage canisters: Insights for lightweight assemblies

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  33. A tool to conjecture hydrogen storage capacity of high entropy alloys

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  34. Research progress in solid-state hydrogen storage alloys: A review

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  35. Discovering elemental effects on hydrogen storage in TiMn2-type alloys using explainable machine learning

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  36. The enthalpy changes for hydrogenation/dehydrogenation of Mg-based alloys

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  37. Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

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  38. Solid-state hydrogen storage alloys for production-storage and transportation-application coupling at ambient temperature: A review

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  39. FIND: a forward–inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven machine learning

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  40. Hydrogen bonding system in metal alloys

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  41. Advancing safety of hydrogen polymeric tanks: A review of permeation mitigation, leak detection, and smart monitoring

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  42. Smart screening of hydrogen storage capacities in MOFs using a tailored machine learning

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  43. Amorphous High-Entropy Alloys as Innovative Materials for Hydrogen Energy: State of the Art and Development Prospects

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  46. A workflow-centric review of artificial intelligence in solid-state hydrogen storage: toward trustworthy AI for materials design

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  47. Explainable ensemble learning reveals site-driven d z 2 orbital occupancy tuning for enhanced hydrogen evolution on metal-doped Ni-loaded BN catalysts

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  49. Interpretable Ensemble Learning Reveals the d z 2 -Orbital-Regulated H Adsorption Mechanism of Metal-Doped Ni/BN Catalysts

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  50. Breaking small sample data barriers: Machine learning prediction of hydrogen storage capacity in Mg-based alloys via data augmentation

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