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RESEARCH PAPER · Computational Condensed Matter · 2025

Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review

Dilshod Nematov, Mirabbos Hojamberdiev

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Machine learning-driven materials discovery: Unlocking next-generation functional materials – A reviewOriginalThe Role of Surprise in AI-Driven Materials Discovery0Temporal Myopia in AI-Accelerated Materials Research0Out-of-Distribution Generalization in Materials AI: A Systematic Review of Domain Shift, Robustness, and What Remains Unsolved0The Problem of Unbounded Search Spaces in Conceptual Materials Discovery0Machine learning-enabled optoelectronic material discovery: a comprehensive review31Quantitative evaluation of the dependence of the Portevin-Le Chatelier effect on temperature and strain rate in an Al–Mg alloy (AA5083-H111): Insights from machine learning3Machine learning-driven advances in nanotechnology: From materials design to process optimization – A review18A critical review on electronic materials properties and multifunctional applications5A Decade of Data-Driven Materials Engineering: Progress, Remaining Gaps, and Unlearned Lessons0Design of MnO 2 -based catalysts with activity approaching Pt/C via machine learning0Interfacial design principles of two-dimensional graphene-based heterostructures for lithium- and sodium-ion battery anodes0Prediction and Joint Optimization of Hardness and Thermal Conductivity of Aluminum-Copper Alloys Based on Machine Learning0Predicting Materials Using Variational Autoencoders: A Systematic Literature Review8Materials Informatics: Emergence to Autonomous Discovery in the Age of AI15Application of multi-agent in energy materials research0Commercialization path of data-driven green catalytic technology: the application of machine learning in technology lifecycle management5Machine-learning prediction of thermoelectric properties using high-entropy descriptors1Surface modification strategies for Ni–Ti-based thermosensitive shape memory alloys: a review of techniques, trade-offs, and performance1Artificial Intelligence-Driven Materials Design for Next-Generation Sustainable Energy Technologies15Machine learning prediction of multi-step hydrogenation energetics in transition metal-doped phosphorene2Machine learning applications in metallic materials: Recent advances and future perspectives8From Data to Discovery: Challenges and Solutions in AI-Driven Materials Science0TRACER: a reliability-first GemNet baseline for trustworthy computational materials discovery0Machine Learning and Domain Adaptation for Material Generalization in Electrocatalyst Evaluation: A Comprehensive Review2Permutation-invariant deep learning for predicting superconducting critical temperatures from chemical composition1Accelerating Computational Material Discovery and Learning: First Experiences with MatterSim0Precision orientation of hydrogen evolution performance of iron-based electrocatalysts empowered by machine learning: From electronic structure regulation to rational construction0Accelerating accurate multi-property prediction of perovskites via meta-heuristic algorithm tuned machine learning0Artificial Intelligence‐Driven Nanomaterials: Pioneering Innovations in Energy Conversion and Storage2Review on composites against atomic oxygen erosion: Protection mechanism and material system0A Critical Assessment of Bonding Descriptors for Predicting Materials Properties1Improving the design efficiency of electrocatalysts for nitrate reduction by data-driven machine learning insight0Artificial Intelligence for Sustainable Ceramic and Refractory Materials: A PRISMA-Guided Systematic Review of Emerging Design Strategies, Industrial Applications, and Circular Raw Material Utilization0Review: Two-dimensional insulating nanofillers for thermally conductive, electrically insulating polymer composites for battery energy storage systems0Familywise Feature Importance Stability in Chemical and Materials Machine Learning1A hybrid PROSAIL inversion framework for winter wheat LCC using hyperspectral data and transfer learning0Functionalization of Nanostructures: Unlocking Optical, Electronic and Biomedical Potential0The Digital Desalination Lab: Revolution or Overpromise?0Recent advances in biomimetic fusion strategies for multi-source heterogeneous data in food detection and safety assessment: A critical review0A feedforward neural network based framework of the cyclic piezoresistive response of additively manufactured embedded strain sensors0From Explainable Machine Learning to Physics-Guided Design Rules for Inorganic Phosphors0Artificial Intelligence in Smart Photovoltaic Systems for High-Efficiency Energy Conversion: A Systematic Review0Research progress of chiral organic semiconductors for circularly polarized light detectors0Research progress of molecular perovskite as a new platform for multicomponent energetic crystals0Multi-target machine learning and deep learning framework for predicting structural, energetic, and magnetic properties of Ternary Heusler alloys using site-resolved descriptors0Computational design of quantum and functional materials for next generation electronic and energy technologies0Heusler intermetallic alloys for electrochemical energy conversion: Electronic structure engineering, structure–reactivity relationships, and rational electrocatalyst design0Intelligent seismic fragility assessment and interpretability analysis of tunnel engineering0Machine learning-driven optimization of lead-free Rb2PtxPd1-xBr6 dual-absorber photodetectors0A Comprehensive Review of Tribovoltaic Nanogenerators for Real‐World Multidisciplinary Applications, Challenges, and Future Perspectives0

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