All Articles

Deep learning for enhanced porosity detection in AZ91 magnesium alloys using windowed perception and aggregated sensing

Prediction of thermal conductivity in multi-component magnesium alloys based on machine learning and multiscale computation

High-throughput screening of phosphide compounds for potassium-ion conductive cathode application

Industrial big data analysis strategy based on automatic data classification and interpretable knowledge graph

Unveiling defect physics in gapped metals: a theoretical investigation into defect formation and electronic structure interplay

Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches

Cosolvent-involved hybrid solvation models for aqueous Zn-ion electrolytes: a case study of ethylene glycol-H₂O-ZnSO₄ system

Integrating sequence and chemical insights: a co-modeling AI prediction framework for peptides

Machine-learning prediction of facet-dependent CO coverage on Cu electrocatalysts

Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development

A property-oriented self-decision design strategy of low-alloyed rare earth-free magnesium alloys with a good strength-ductility synergy based on machine learning

PINK: physical-informed machine learning for lattice thermal conductivity

Towards mechanical performance paradox and behind thermo-kinetic origins of aluminum alloys with additional solutes (X = Mg, Cu and Si) from atomistic simulations

Design of Fe₂Mo@γ-GDY triatomic catalyst for electrocatalytic urea synthesis of N₂ and CO: a theoretical study

AI in single-atom catalysts: a review of design and applications

An optimized strategy for density prediction of intermetallics across varied crystal structures via graph neural network

Improved hardness prediction for reduced-activation high-entropy alloys by incorporating symbolic regression and domain adaptation on small datasets

Interpretable model of dielectric constant for rational design of microwave dielectric materials: a machine learning study