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Review
Open access
Jul 2026
Machine Learning-Assisted Optimization and Application of Carbon-Based Emitters
Machine learning (ML) is progressively being integrated into materials science, exhibiting great potential for optimizing chemical synthesis and structural regulation, thereby accelerating intelligent design and efficient exploration of novel materials. Carbon-based emitters (CBEs), as emerging functional materials, have attracted considerable attention due to their tunable photoluminescence properties, abundant precursor sources, and structural versatility. To date, extensive research on CBEs has generated a substantial data foundation, laying the groundwork for ML driven structural screening and property prediction. Although the application of ML in CBEs remains in its early stages compared to that of inorganic semiconductors and organic-inorganic hybrid perovskites, its potential to accelerate material screening and reveal structure-property relationships is becoming increasingly evident. Given the transformative role ML has played in other functional materials, it is expected to drive a paradigm shift in CBEs research from conventional trial-and-error approaches to data-driven, intelligence-guided design, substantially accelerating material discovery and expanding their functional applications. Therefore, this review systematically summarizes recent advances in ML applications in CBEs, focusing on the general ML workflow, property prediction and structural design strategies for organic small-molecules and carbon dots (CDs), and their applications in organic light-emitting diodes (OLEDs), quantum dot light-emitting diodes (QLEDs), information encryption, biomedicine, and sensing. Finally, this review discusses the key challenges currently facing the field and offers perspectives on future directions for ML-driven CBEs research, aiming to provide guidance for the rational design and efficient development of such materials.
Unknown authors
· Materials Matter · 0 citations
Jul 2026
Predicting dielectric constants of crystalline materials using explainable machine learning and composition-aware feature engineering
An explainable machine-learning framework was developed for dielectric constant prediction using 52,168 crystalline materials extracted from the Joint Automated Repository for Various Integrated Simulations (JARVIS-DFT) database, demonstrating the complementary roles of electronic structure and elemental chemistry.
D. Pundhir, Ashok Kumar
· Applied Physics A · 0 citations
Open access
Aug 2026
Data-driven graph neural networks guide codoping for stable Ni-rich layered cathodes
Ni-rich layered oxide cathode materials have emerged as promising candidates for next-generation mainstream high-energy nonaqueous lithium-based batteries because of their inherent advantages in terms of specific capacity. However, the delicate layered structure is more susceptible to both crystal and morphological structure degradation under electro-chemo-mechanical stresses at high states of delithiation, leading to an unsustainable cycle life and vulnerable safety hazards. To address these issues, 20 extensively utilized doping elements are incorporated into a LiNi0.9Co0.1Mn0.1O2 (NCM90) cathode to generate a critical dataset with 20 descriptors comprising its intrinsic, structural and morphological features. Furthermore, we develop a designed multidimensional graph differential neural network (MDGNN) model trained on the former dataset to fit the doping laws, which results in an error rate in predicting two key performance metrics, e.g. specific capacity and capacity retention, of an arbitrary dopant-modified NCM90 cathode. Combined with the MDGNN model and traditional chemical principle, 1.2 mol% Al3+, 0.3 mol% Zr4+ and 0.5 mol% Sb5+ are elaborately doped into the NCM90 cathode to stabilize its electrochemical and thermal properties, and the related operation mechanism is systematically investigated via characterization. The developed 7.3 Ah Al1.2Zr0.3Sb0.5-NCM90||graphite pouch cell attains 87.3% capacity retention after 3000 cycles at 1.0/1.0 C, verifying the practical feasibility of our MDGNN model. These results open a viable avenue for the data-driven design of high-performance battery materials.
Mengyu Tian, Yang Li, Zhe-Wen Xu et al.
· AI for Science · 0 citations
Review
Open access
Aug 2026
Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.
Due to their high specific energy, lithium-metal batteries (LMBs) are widely regarded as the promising next-generation energy storage devices. Nevertheless, their practical applications are plagued by the challenges of irregular deposition and dissolution, coupled with the high chemical reactivity of lithium electrodes. Extensive research has focused on the stabilization of electrode-electrolyte interfaces as the key strategy to achieve improved battery performance. However, the exploration process via traditional "trial-and-error" methodologies is impeded by the long period and high cost of the tedious experiments. Machine learning (ML) technologies have become a mainstream force, redefining the revolutionary paradigm, enabling intelligently capturing the complex structure-performance relationships across vast compositional and structural spaces. Herein, ML applications in the discovery of electrolytes, electrodes, and interface engineering are reviewed, with the emphasis on ML-driven investigation workflow covering data collection, feature engineering, model selection and ML-assisted simulations. Moreover, task-oriented ML technologies for expediting materials screening, informative descriptors extraction, mechanistic elucidation, and reverse design of novel electrodes and electrolytes are highlighted. Finally, future trajectories centered on multiscale materials simulation, multimodal modeling, and intelligent platform establishment to overcome persistent challenges are outlined, aiming at catalyzing the rational design of highly stable lithium electrode-electrolyte interface for long-lasting rechargeable LMBs.
Xiaorui Liu, Qingyu Li, Jianghao Liang et al.
· Small · 0 citations
Aug 2026
Machine learning-assisted first-principles investigation of structural, electronic, and photovoltaic properties of perovskite materials
A unified multiscale system that entails the implementation of density functional theory (DFT), machine learning (ML), and device-level simulation to hasten the search and development of high-performance perovskite solar cell materials is presented.
Sameer Pandey, N. Shukla, Vishal K. Sharma et al.
· Applied Nanoscience · 0 citations
Review
Open access
Aug 2026
Toward predictable hydrochar properties at scale: a critical review of design of experiments-guided machine learning in hydrothermal carbonization
Hydrothermal carbonization (HTC)has emerged as a versatile platform for converting wet biomass into functional carbonaceous solids whose properties can be tuned across energy- and material-relevant applications. Under subcritical aqueous conditions, HTC proceeds through coupled dehydration, decarboxylation, polymerization, and recondensation reactions. These transformations reshape elemental composition, surface chemistry, and microstructure, enabling the production of hydrochars with tunable fixed carbon content, heating value, porosity, and surface functionality. Yet, the complexity of these reaction networks, compounded by feedstock heterogeneity and strongly nonlinear parameter interactions, continues to limit predictive control of hydrochar properties and hampers reproducible, application-driven materials design. In this review, we synthesize and critically assess how design of experiments (DoE) and machine learning (ML) can be combined to move HTC from empirical tuning toward data-informed, property-targeted engineering of hydrochar materials. We show that DoE frameworks enable statistically efficient exploration of multifactorial operating spaces and generate structured datasets that quantify the main effects and interactions of key variables, such as temperature, residence time, solid-to-liquid ratio, pressure, and catalysis. Building on these data, machine learning algorithms, including artificial neural networks, ensemble methods, boosting, and Bayesian approaches, capture high-order nonlinearities beyond classical response surface models and improve the prediction of material-critical outputs, notably mass yield, higher heating value (HHV), fixed carbon, carbon retention, and textural and chemical descriptors linked to adsorption performance and electrochemical relevance. We highlight that the DoE–ML coupling is particularly valuable for multi-objective optimization, where energy densification must be balanced against the retention of functional groups and the development of porosity, depending on whether hydrochars are targeted as solid fuels, adsorbents for water and gas treatment, or precursors for advanced carbon materials. Finally, we discuss the key bottlenecks that currently limit transferability and industrial robustness, including data quality and comparability across studies, the interpretability of predictive models, and the systematic treatment of biomass variability. We also outline methodological directions for developing more reliable hybrid DoE–ML strategies to accelerate rational design of hydrochar materials.
Faiçal El Ouadrhiri, Amal Lahkimi
· Frontiers in Chemical Engine... · 0 citations