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Hot deformation behavior investigation of heat-resistant aluminum matrix composite based on Arrhenius model and machine learning

Phase-field-informed machine learning on creep behavior of Ni-based single-crystal superalloys

Effect of material properties on the thermal responses of the carbonization and pyrolysis layers of polymer matrix composites for charring-ablators

Accelerated discovery of high-performance small-molecule hole transport materials via molecular splicing, high-throughput screening, and machine learning

Symbolic regression accelerates the discovery of quantitative relationships in rubber material aging

Uncertainty aware design space modeling for sample efficiency in material design of bainitic steels

FT²DP: large atomic model fine-tuned machine learning potential for accelerating atomistic simulation of iron-based Fischer-Tropsch synthesis

Multi-objective optimization in machine learning assisted materials design and discovery

How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell?

Design of high energy storage ferroelectric materials by phase-field simulations

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