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Using first-principles electronic structure calculations, we have studied the dependence of the Curie temperature on external hydrostatic pressure for random Ni2MnSn Heusler alloys doped with Cu and Pd atoms, over the entire range of dopant…

Understanding the thermal behavior of additive manufacturing (AM) processes is crucial for enhancing the quality control and enabling customized process design. Most purely physics-based computational models suffer from intensive…

If a product deviates from its desired properties in the injection moulding process, its root cause analysis can be aided by models that relate the input machine settings with the output quality characteristics. The machine learning models…

Cuprates, a member of high-Tc superconductors, have been on the long-debate on their superconducting mechanism, so that predicting the critical temperature of cuprates still remains elusive. Herein, using machine learning and first…

Superconductors have been among the most fascinating substances, as the fundamental concept of superconductivity as well as the correlation of critical temperature and superconductive materials have been the focus of extensive investigation…

Using the Swendsen and Wang algorithm, high accuracy Monte Carlo simulations were performed to study the concentration dependence of the Curie temperature in binary, ferromagnetic Ising systems on the simple-cubic lattice. Our results are…

A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural net model is presented. The training of the feed-forward Neural Network (NN) enables the…

With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly machine learning algorithms. We investigated the predictive…

This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance…

Prediction of the Curie temperature is of significant importance for the design of ferromagnetic materials. Even though the Curie temperature has been estimated using the Heisenberg model, magnetic exchange coupling parameters widely used…

The advance of Machine Learning (ML) has led to a strong interest in this technology to support decision making. While complex ML models provide predictions that are often more accurate than those of traditional tools, such models often…

Explainable Artificial Intelligence (XAI) has become a widely discussed topic, the related technologies facilitate better understanding of conventional black-box models like Random Forest, Neural Networks and etc. However, domain-specific…

We review recently developed two-step approach for description of electronic and magnetic properties of a new class of materials, the diluted magnetic semiconductors. In the first step we construct, on the basis of the state-of-the-art…

Magnetism prediction is of great significance for Fe-based metallic glasses (FeMGs), which have shown great commercial value. Theories or models established based on condensed matter physics exhibit several exceptions and limited accuracy.…

Nanoscale layered ferromagnets have demonstrated fascinating two-dimensional magnetism down to atomic layers, providing a peculiar playground of spin orders for investigating fundamental physics and spintronic applications. However,…

An accelerated model-based information theoretic approach is presented to perform the task of Magnetic Resonance (MR) thermal image reconstruction from a limited number of observed samples on k-space. The key idea of the proposed approach…

Composites are amongst the most important materials manufactured today, as evidenced by their use in countless applications. In order to establish the suitability of composites in specific applications, finite element (FE) modelling, a…

The availability of Martian atmospheric data provided by several Martian missions broadened the opportunity to investigate and study the conditions of the Martian ionosphere. As such, ionospheric models play a crucial part in improving our…

A new scenario of the mechanism of intriguing ferromagnetic properties in Mn-doped magnetic semiconductor (Ga,Mn)As is examined in detail. We find that magnetic features seen in zero-field cooled and field cooled magnetizations are not…

Various combinations of characteristic temperatures, such as the glass transition temperature, liquidus temperature, and crystallization temperature, have been proposed as predictions of the glass forming ability of metal alloys. We have…