DISA

When? Friday April 25th 14-16
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/QJ6GrkgywhZMbAKt8 by April 23 (especially important if you plan on attending onsite so we have fika for everyone)

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Enhancing Efficiency in Industry: Automation, Analytics, and Digital Twins – Arslan Musaddiq
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Machine-Learning Models for Two-Dimensional Antiferromagnetic Materials – Shahid Sattar
15.50 -16.00 Sum up and plan for our seminars in May

Abstracts

Enhancing Efficiency in Industry: Automation, Analytics, and Digital Twins –Arslan Musaddiq
This presentation will be about ongoing industry collaborations focused on data-driven optimization in manufacturing, energy, and logistics. These projects aim to improve operational efficiency, enhance decision-making, and drive digital transformation across various sectors. The presentation will cover the automation of waste input measurement and real-time thickness monitoring in construction board production, energy data analysis for baseload identification and prediction, digital twin development for sawmill process optimization, and scheduling optimization for mobile blood donation units. The seminar will explore the challenges, methodologies, and practical impact of integrating smart systems into traditional industries.

Machine-Learning Models for Two-Dimensional Antiferromagnetic Materials – Shahid Sattar
Two-dimensional antiferromagnets (2D AFMs) recently gained tremendous scientific interest owing to their use in next-generation spintronic devices. In this talk, I will discuss about our recent effort to develop machine-learning (ML) models for 2D AFMs. More specifically, how ML models together with molecular dynamics simulations can be effectively used to capture surface reconstructions in topological 2D AFMs [1]. Additionally, I will show how ML models can be employed to compute thermal properties and heat transport in 2D MnX and Janus XMnY (X,Y=S, Se, Te). Finally, I will talk about potential new avenues which can be explored combining first-principles calculations and ML models.

[1] S. Sattar, D. Hedman and C. M. Canali, Phys. Rev. Research (2025).