Most Cited Papers In Last Two Years

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

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

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

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

Superior single-atom and single-cluster catalysts towards electrocatalytic nitrogen reduction reactions: a theoretical perspective

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

Machine learning assisted crystal structure prediction made simple

Machine learning-enabled optoelectronic material discovery: a comprehensive review

Machine learning driven design of high-performance Al alloys

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

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

Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development

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

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

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

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

Lithium-ion battery health prognosis via electrochemical impedance spectroscopy using CNN-BiLSTM model

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