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Machine learning-driven design and optimization of electronic packaging: applications and future developments

Heterogeneous catalyst design by generative models

Data-driven OLED candidate design: a generative model from independent-property domains to the comprehensive performance enhancement

A critical review of machine learning interatomic potentials and Hamiltonian

Machine learning for predictive design and optimization of high-performance thermoelectric materials: a review

Farthest point sampling in property designated chemical feature space as an effective strategy for enhancing the machine learning model performance for small scale chemical dataset

Effects of nonlinearity and inter-feature coupling in machine learning studies of Nb alloys with center-environment features

Exploring generic fractions of multi-origin asphalts and revisiting the linkage to their bulk properties via machine learning

Machine learning-enabled optoelectronic material discovery: a comprehensive review

Neuroevolution potential for thermal transport in silicon carbide

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