The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …

The fields of machining learning and artificial intelligence are rapidly expanding, impacting
nearly every technological aspect of society. Many thousands of published manuscripts …

Traditional strategies for designing new materials with targeted property including methods
such as trial and error, and experiences of domain experts, are time and cost consuming. In …

Advancement in technology has created wide opportunities for the researchers to utilize
artificial intelligence in various fields. Numerous attempts have been made in the use of …

In medium/high entropy alloys, their mechanical properties are strongly dependent on the
chemical-elemental composition. Thus, searching for optimum elemental composition …

As the fourth paradigm of materials research and development, the materials genome
paradigm can significantly improve the efficiency of research and development for austenitic …

To improve the fatigue fracture resistance of high-speed railway wheels, this paper studies
the matching optimization design of microscopic crystal parameters of wheel materials …

Data-mining based machine learning (ML) method is emerging as a strategy to predict
aluminium (Al) alloy properties with the promise of less intensive experimental work …

Heavy metal ions in aqueous solutions are taken into account as one of the most harmful
environmental issues that ominously affect human health. Pb (II) is a common pollutant …

An artificial neural network (ANN) model was designed to correlate the complex relations
among composition, temperature, and mechanical properties of 18Cr-12Ni-Mo austenitic …