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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, ha...
Unknown authors
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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.
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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 str...
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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...
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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.
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Review
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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, polymerizat...
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