Most Cited Papers In Last Two Years

Transformative strategies in photocatalyst design: merging computational methods and deep learning

Applications of machine learning method in high-performance materials design: a review

Machine learning assisted crystal structure prediction made simple

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

Machine learning descriptors for crystal materials: applications in Ni-rich layered cathode and lithium anode materials for high-energy-density lithium batteries

Data-driven strategy for bandgap database construction of perovskites and the potential segregation study

High hydrogen evolution activities of dual-metal atoms incorporated N-doped graphenes achieved by coordination regulation

Machine learning-assisted prediction, screen, and interpretation of porous carbon materials for high-performance supercapacitors

Local environment interaction-based machine learning framework for predicting molecular adsorption energy

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

Machine learning driven design of high-performance Al alloys

Thermal stability prediction of copolymerized polyimides via an interpretable transfer learning model

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

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

Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development

Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks

Knowledge assisted machine learning to clarify pore influence on fatigue life of forging/additive hybrid manufactured Ti-17 alloy

Multi-model integration accelerates Al-Zn-Mg-Cu alloy screening