All Articles

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

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