In recent decades, the use of artificial intelligence (AI) techniques in the field of materials
modeling has received significant attention owing to their excellent ability to analyze a vast …

Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …

Computational materials design is a rapidly evolving field of challenges and opportunities
aiming at development and application of multi-scale methods to simulate, predict and select …

Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …

In material science, traditional experimental and computational approaches require
investing enormous time and resources, and the experimental conditions limit the …

Big data is usually needed for a deep learning method to predict the properties of materials,
but, in practice, only limited data sets are available for engineering materials. In this study …

The paradigm of advanced materials has grown exponentially over the last decade, with
their new dimensions including digital design, dynamics, and functions. Materials modeling …

For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …

We present a web-based framework based on the R shiny package with functional back-end
server in machine learning methods. A 4-tiers architecture is programmed to achieve users' …

The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing
the field of materials engineering thanks to their power to predict material properties, design …