Research Projects

Deciphering laser-microstructure interaction in multicomponent alloys (DECLARMIMA)

 Despite the growing interest in additive manufacturing (AM), 3D-printed products remain a small part of the manufacturing economy, hindered by a lack of understanding of laser-microstructure interactions. The DECLARMIMA project addresses this through multi-scale computations, experiments, and machine learning (ML) on Sn-Ag-Cu-X,  Al-Ni-Fe-Cr alloys and other multicomponent alloys processed with laser. The in-silico approach integrates molecular dynamics, mesoscale phase-field modeling, and tensor decomposition to analyze energy transfer, material properties, and laser profiles. The theory and experiment informed ML techniques will predict the actual profile of beam intensity, characterisitics of melt-pool, and defect variables, while Bayesian methods will quantify the uncertainties involved. The project will delivers descriptors for microstructural evolution and  physics informed digital twins, enabling efficient diagnostics and optimization for AM, driving innovation in 3D printability and material design.

Researchers: Anil Kunwar (Principal Investigator), Nele Moelans (Collaboration through International Cooperation),  Akash Deshmukh (2023-2024), Hana Beyene Mamo (2022-2024), Oktawian Bialas (2022-2024), Sachin Poudel, Upadesh Subedi.

Funding:  the National Science Centre, Poland (UMO-2021/42/E/ST5/00339)

Learning the Physics of Dendrite Growth in Lithium-Ion Batteries: An Attention Mechanism Approach for Prevention and Mitigation  (DENDRITEPHASE)

Lithium ion batteries (LIBs) are considered as the materials of the future when it comes to the efficient energy storage during utilization of renewable energy technologies. Lithium  dendrites are responsible for problems like short circuits, catastrophic failures and fires, electrolyte decomposition, and loss of active lithium in these batteries. The formation of dendrites is an interfacial process spanning numerous length- and time scales; and regardless of decades of research, their composition, structure and formation still present a significant conundrum. The achievement of completely dendrite-free battery interfaces can be possible only through the correct understanding of the fundamental mechanisms governing the dendritic evolution. This research proposal presents a combination of approaches linking the microstructure-property and process-kinetics relationship in the different material phases of an electrochemical battery through integrated experiments, computations and artificial intelligence, to subsequently demystify the dendrite evolution mechanisms. Multi-physics finite element simulations and atomistic calculations will be combined. The computational datasets on structure, properties and behavior of battery materials, combined with the experimental datasets of temperature, voltage and other physical quantities, will be employed to construct attentive generative AI models.

Researchers: Anil Kunwar (co-Principal Investigator), Nele Moelans (co-Principal Investigator).

Funding:  the National Science Centre, Poland (UMO-2023/51/I/ST11/02716), Research Foundation – Flanders (G000225N)