Discovering new materials with excellent performance is a hot issue in the materials
genome initiative. Traditional experiments and calculations often waste large amounts of …

With the development of the materials genome philosophy and data mining methodologies,
machine learning (ML) has been widely applied for discovering new materials in various …

Exploring the relationships between the properties of steels and their compositions and
manufacturing parameters is extremely crucial and indispensable to understanding the …

Artificial neural networks are an effective and frequently used modelling method in
regression and classification tasks in the area of steels and metal alloys. New publications …

Currently, the growth of material data from experiments and simulations is expanding
beyond processable amounts. This makes the development of new data‐driven methods for …

Thermoelectric materials have been widely recognized as a simple approach to harness
green energy by converting thermal gradients into electrical energy. However, the intricate …

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

In this study, artificial neural network (ANN) and multi-objective genetic algorithm (GA) are
employed in tandem to design dual-phase (DP) steel with improved performance. Six …

Biodegradable lubricant based on the blend of various vegetable oils with different nano
friction modifier in combination is designed using computational intelligence technique and …

The accurate prediction of tensile properties has great importance for the service life
assessment and alloy design of RAFM steels. In order to overcome the limitation of …