Junaid Akhter | Searcharxiv

arXiv2026

Influence of Hydrogen on Dislocation Relaxation in BCC Iron: Atomistic Mechanisms and Implications

In this study, the influence of pure dislocation and hydrogen-dislocation interactions on anelastic response or internal friction relaxation peaks in bcc-iron was investigated. These relaxations are primarily governed by thermally activated kink nucleation and kink migration events. An atomistic multiscale framework, coupling molecular dynamics (MD) and kinetic Monte Carlo (KMC) simulations, was developed to investigate the underlying atomistic mechanisms behind dislocation-relaxation peaks. MD simulations revealed that the presence of hydrogen atoms near the dislocation core facilitates the kink nucleation process by reducing the nucleation barrier while enhancing the barrier for dislocation migration. The KMC model captured Snoek-Koster peaks arising from the Cottrell atmosphere formed by hydrogen atoms and clusters around the dislocation core, providing insights into the atomistic mechanisms controlling these relaxations. Furthermore, the proposed computational scheme elucidated a unique linear relationship between hydrogen content and the internal friction loss factor, offering a methodology for hydrogen detection and quantification.

cond-mat.mtrl-sci↗

arXiv2024

Common pitfalls to avoid while using multiobjective optimization in machine learning

Recently, there has been an increasing interest in the application of multiobjective optimization (MOO) in machine learning (ML). This interest is driven by the numerous real-life situations where multiple objectives must be optimized simultaneously. A key aspect of MOO is the existence of a Pareto set, rather than a single optimal solution, which represents the optimal trade-offs between different objectives. Despite its potential, there is a noticeable lack of satisfactory literature serving as an entry-level guide for ML practitioners aiming to apply MOO effectively. In this paper, our goal is to provide such a resource and highlight pitfalls to avoid. We begin by establishing the groundwork for MOO, focusing on well-known approaches such as the weighted sum (WS) method, alongside more advanced techniques like the multiobjective gradient descent algorithm (MGDA). We critically review existing studies across various ML fields where MOO has been applied and identify challenges that can lead to incorrect interpretations. One of these fields is physics informed neural networks (PINNs), which we use as a guiding example to carefully construct experiments illustrating these pitfalls. By comparing WS and MGDA with one of the most common evolutionary algorithms, NSGA-II, we demonstrate that difficulties can arise regardless of the specific MOO method used. We emphasize the importance of understanding the specific problem, the objective space, and the selected MOO method, while also noting that neglecting factors such as convergence criteria can result in misleading experiments.

cs.LG↗