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Machine Learning-Assisted Screening of Hexagonal 2D van der Waals Bilayers for Photocatalysis

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Machine Learning-Driven Discovery of Key Descriptors for CO2Activation over Two-Dimensional Transition Metal Carbides and Nitrides

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The photocatalytic degradation performance of Dion-Jacobson-type layered perovskites AMCa2Ta3O10 (AM=Rb, Cs): A DFT study

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Unveiling the effect of solvent for hydrogen evolution in Pt-doped MXenes and corresponding high-entropy phase

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Research progress of first principles calculations in energy storage materials

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MXene-Based Single-Atom Catalysts for Electrochemical Reduction of CO2 to Hydrocarbon Fuels

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Machine learning-assisted the Ag/Ni(OH)2 heterostructure design for boosting electrocatalytic hydrogen evolution through charge redistribution

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Accelerated discovery of magnesium intermetallic compounds with sluggish corrosion cathodic reactions through active learning and DFT calculations

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Recent Advances in Biomedical Applications of MXene‐Integrated Electrospun Fibers: A Review

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Accelerated discovery of MM′XT2 MXenes for catalysis, electronics, and energy storage using supervised machine learning

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MXene-based hybrid materials for electrochemical and photoelectrochemical H2 generation

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Accelerating ionic liquid research in perovskite solar cells through machine learning:Opportunities and challenges

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Theoretical modelling of the Hydrogen evolution reaction on MXenes: A critical review

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Heterojunction of MXenes and MN4–graphene: Machine learning to accelerate the design of bifunctional oxygen electrocatalysts

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Double transition metal MBenes: A family of 2D MXenes with tunable magnetic properties

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Enhanced uptake of antimonite onto Fe-Zr oxide-loaded MXene: Mass transfer and machine learning data mining

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Machine Learning Assisted Screening of MXene with Superior Anchoring Effect in Al–S Batteries

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Tribology of MXene Materials: Advances, Challenges, and Future Directions

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Novel Janus-MNbCO2 (M = Mo, Ta) for high-performance Li-Ion batteries: Insights from first-principles calculations

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Emerging Photoreforming Process to Hydrogen Production: A Future Energy

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InVO4-Decorated Ti3C2 MXene for Efficient Photocatalytic Hydrogen Evolution

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Defects in MOFs for Photocatalytic Water Reduction to Hydrogen Generation: From Fundamental Understanding to State‐of‐Art Materials

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Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction

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Data-Driven High-Throughput Screening of High-Performance Single-Atom Catalysts for Hydrogen Evolution and Hydrogen Sensing

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Charge-Transfer Modulation on CdS Artificial Leaf for Efficient Hydrogen Generation

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Integrating Machine Learning and Molecular Simulation for Material Design and Discovery

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Unveiling Termination Preferences and Screening of Structural Space in Multi-Metal MXenes

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Integrating artificial intelligence with electrochemical science for advanced energy storage

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Artificial intelligence shaping the future of hydrogen technologies

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MXenes and heterostructures-based electrocatalysts for hydrogen evolution reaction: Recent developments and future outlook

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Machine learning-assisted prediction and optimization of electrocatalytic activity

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Developing machine learning for heterogeneous catalysis with experimental and computational data

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Artificial Intelligence Enabled Self‐Powered Acoustics Sensing: From Energy Harvesting to Healthcare Monitoring

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An overview about neural networks potentials in molecular dynamics simulation

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Catalysis in the digital age: Unlocking the power of data with machine learning

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Unveiling structure-performance relationships in HER catalysts for sustainable and scalable hydrogen production

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Measuring the impact of materials composition on MXene property prediction using explainable AI

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Accelerated screening of sensitive and selective MoO3-based gas sensing materials by combining first-principles and machine learning approach

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Enhancing HER catalyst screening of modified MXenes through DFT and machine learning integration

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Risk Identification and Countermeasures of Wetland Park Construction Project

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Machine learning-assisted screening of transition metal-doped TMDs for binding energy and charge transfer prediction

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Electrochemical properties of MXenes and applications

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A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

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A review on catalyst convergence: Unleashing the potential of MXenes for CO2 electrochemical reduction into high-value liquid product

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Machine learning prediction of bandgap and formation energy in two-dimensional metal oxides

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Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy

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DFT and hybrid classical–quantum machine learning integration for photocatalyst discovery and hydrogen production

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Accelerating the Discovery of g-C3N4-Supported Single Atom Catalysts for Hydrogen Evolution Reaction: A Combined DFT and Machine Learning Strategy

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Catalytic Performance of Single and Double Metal MXenes for the Hydrogen Evolution Reaction

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Defect and non-metallic doping co-engineering in two-dimensional transition-metal carbide-based electrocatalysts for renewable energy conversion

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MXene-derived materials as electrocatalysts for hydrogen evolution reaction: A review

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Machine learning driven advancements in catalysis for predicting hydrogen evolution reaction activity

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The rise of the machines: A state-of-the-art technical review on process modelling and machine learning within hydrogen production with carbon capture

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Emerging ZnO Semiconductors for Photocatalytic CO2 Reduction to Methanol

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First principles and interpretable machine learning aided the exploration of defective MXene for nitrogen reduction reaction

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Recent progress in density functional theory and machine learning for predicting MXene properties

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From density functional theory to machine learning: Emerging paradigms in energy materials discovery

M.V. Jyothirmai, Shrish Nath Upadhyay, S. Muthu Krishnan, Sudipta Roy and Jayant K. Singh

Journal of Power Sources, 2026, 671, 239478
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Hf3C2O2 MXene: A promising NH3 gas sensor with high selectivity/sensitivity and fast recover time at room temperature

Qianku Hu, Wei Liu, Dandan Li, Qinghua Wu, Yukai Chang, Junkai Wang, Qixun Xia, Libo Wang and Aiguo Zhou

Materials Today Communications, 2024, 40, 109467
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MXgap: A MXene Learning Tool for Bandgap Prediction

Diego Ontiveros, Sergi Vela, Francesc Viñes and Carmen Sousa

ACS Catal., 2025, 15, 14403
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DFT modeling of electrocatalytic water splitting for improvement of experimental relevance

Sung Uk Chai, Yoonjun Cho, Ki Chul Kim and Jong Hyeok Park

Applied Catalysis B: Environment and Energy, 2026, 386, 126359
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Transforming Catalysis with Machine Learning: Emerging Tools and Next-Gen Strategies

Pengxin Pu, Haisong Feng, Xin Song, Si Wang, Jie J. Bao and Xin Zhang

ACS Appl. Mater. Interfaces, 2025
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Recent Advances in Single‐Atom Catalyst for Solar Energy Conversion: A Comprehensive Review and Future Outlook

Saad Mehmood, Saddam Sk, B. Moses Abraham, Mohsen Ahmadipour, Ujjwal Pal and Joydeep Dutta

Adv Funct Materials, 2025, 35
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Machine Learning Toolkits and Frameworks for Materials Design

B. Moses Abraham and Yury Gogotsi

WIREs Comput Mol Sci, 2026, 16
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Investigation of optical and thermodynamic characteristics of double transition metal YMX (M = Ti and Zr; X = C and N) MXenes

Zeynab Amoudeh, Peiman Amiri and Amir Aliakbari

Solid State Communications, 2025, 397, 115805
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Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction

Guohao Yin, Haiyan Zhu, Shanlin Chen, Tingting Li, Chou Wu, Shaobo Jia, Jianxiao Shang, Zhequn Ren, Tianhao Ding and Yawei Li

Molecules, 2025, 30, 759
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Revolutionizing MXene nanomaterials for hydrogen production and storage: Enhancing catalysis, storage, mechanical integrity, and ecosystem compatibility

Poongavanam GaneshKumar, Karthik Panchabikesan, S. Divya and Tae Hwan Oh

Advances in Colloid and Interface Science, 2025, 342, 103528
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Advancements in MXene-based frameworks towards photocatalytic hydrogen production, carbon dioxide reduction and pollutant degradation: Current challenges and future prospects

Zeeshan Ajmal, Asif Hayat, Abdul Qadeer, Yu Zhao, Essam H. Ibrahim, Mahmood ul Haq, Kanwal Iqbal, Mohd Imran, Mohammed Kuku, Iftikhar Hussain, Hamid Ali, Yasin Orooji, John L. Zhou and Teng Ben

Coordination Chemistry Reviews, 2025, 523, 216226
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Machine learning assisted screening of non-metal doped MXenes catalysts for hydrogen evolution reaction

Mei Yang, Changxin Wang, Minhui Song, Lu Xie, Ping Qian and Yanjing Su

International Journal of Hydrogen Energy, 2025, 113, 740
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Machine Learning Accelerated Discovery of Functional MXenes with Giant Piezoelectric Coefficients

Xiaowen Li, Jian Qiu, Heping Cui, Xianping Chen, Jiabing Yu and Kai Zheng

ACS Appl. Mater. Interfaces, 2024, 16, 12731
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