MecaNano Pitching Event + Round Table on Machine Learning Applications (WG1–WG3)

Contribution List

1. Welcome & Objectives

Claus Trost (Erich Schmid Institute of Materials Science of theAustrian Academy of Sciences), Prof. Edoardo Rossi (Università degli Studi Roma Tre)

12/12/2025, 10:00

A short introduction to the event, outlining the goals of the pitching session and the expected outcomes for participants across WG1–WG3.

6. Machine Learning Strategies for Micromechanical Phase Classification from Nanoindentation Data

Laia Ortiz Membrado (Universitat Politècnica de Catalunya)

12/12/2025, 10:15

Pitch presentation

This work investigates how machine learning can be used to classify mechanical phases in WC–Co hardmetals from nanoindentation data. High-speed nanoindentation provides depth-resolved mechanical responses that contain rich information about local microstructural behavior. By focusing on the full hardness–depth curves instead of single-point values, meaningful features can be extracted for...

10. Synthetic Nanomechanical Data and Kernel-Averaged Mechanical Mismatch for Robust Phase Clustering

David MERCIER (Ansys part of Synopsys)

12/12/2025, 10:30

Pitch presentation

We present an unsupervised machine learning framework to enhance phase identification in nanoindentation mapping of multiphase materials, combining physics-informed features with synthetic data generation. Central to this approach is the Kernel-Averaged Mechanical Mismatch (KAMM), a spatial descriptor that quantifies local gradients in elastic modulus (E) and hardness (H). KAMM is incorporated...

16. Correlated nanoindentation mapping and microstructural chracterization of advanced ferritic-austenitic stainless steels.

Rahul Cherukuri (Universität Kassel, Tampere University)

12/12/2025, 10:45

Pitch presentation

Understanding the origins of local mechanical behaviour is crucial for developing novel steel grades. Here, the mechanical properties of metastable, lean alloyed ferritic-austenitic stainless steels were investigated using nanoindentation mapping. The level of metastability was modified through alloying, resulting in different austenite fractions ranging from 17 to 50 %. For nanoindentation, A...

13. Integrating CSM Nanoindentation and Materials Databases for Machine Learning-Based Phase Insights

Tizian Arold

12/12/2025, 11:00

Pitch presentation

A machine learning-assisted approach for phase identification in materials could make use of nanoindentation mapping, particularly continuous stiffness measurement (CSM) data. The richer set of mechanical features accessible through CSM such as hardness, modulus, indentation depth, contact stiffness, energy dissipation, and the evolving H/E ratio combined with freely available materials...

18. Analysis of deformation mechanisms in Ni-based cemented carbides by means of micropillar compression and Machine Learning techniques

Emilio Jiménez-Piqué (Universitat Politècnica de Catalunya), Francesc Barberá-Flichi (CIEFMA, Department of Materials Science and Engineering, Universitat Politècnica de Catalunya - BarcelonaTech, Campus Diagonal Besòs-EEBE, 08019 Barcelona, Spain 2Barcelona Research Center in Multiscale Science and Engineering, Universitat Politècnica de Catalunya, Campus Diagonal Besòs − EEBE, Barcelona, 08019, Spain), Laia Ortiz Membrado (Universitat Politècnica de Catalunya), Luis Llanes (1CIEFMA, Department of Materials Science and Engineering, Universitat Politècnica de Catalunya - BarcelonaTech, Campus Diagonal Besòs-EEBE, 08019 Barcelona, Spain 2Barcelona Research Center in Multiscale Science and Engineering, Universitat Politècnica de Catalunya, Campus Diagonal Besòs − EEBE, Barcelona, 08019, Spain)

12/12/2025, 11:30

Pitch presentation

Cemented carbides are multiphase materials constituted by a hard ceramic phase,
commonly tungsten carbide (WC), embedded in a metallic matrix, being the mostly
used Co. The mechanical properties achieved from these two constitutive phases,
including an excellent hardness, strength, toughness, wear and abrasive resistance,
makes the WC a highly demanded material for industrial applications,...

7. Exploration of the hyperspace of refractory high-entropy alloys using high-throughput materials engineering and machine learning

Krzysztof Wieczerzak (Department of Materials Science, Faculty of Mechanical Engineering and Aeronautics, Rzeszow Universi-ty of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland)

12/12/2025, 11:45

Pitch presentation

There is a growing demand for metallic alloys with high strength and corrosion resistance at elevated temperatures for applications in aerospace, power generation, and chemical industries. With the ad-vancement of modern technologies, the need for metallic materials capable of operating at higher tem-peratures than current Ni-based superalloys continues to increase. Refractory high-entropy...

9. Exploring the extrapolation limits of machine learning models for refractory complex concentrated alloys using high-throughput experiments

Hanna Szebesczyk

12/12/2025, 12:00

Pitch presentation

Refractory complex concentrated alloys (RCCAs) are emerging as key candidates for structural applications under extreme conditions, where both strength and stability beyond 1000 °C are required. Yet, the immense compositional space of these alloys (spanning billions of possible combinations) makes their systematic exploration by conventional methods infeasible. High-throughput materials...

8. Accelerating the discovery of ductile refractory complex concentrated alloys via high-throughput experiments and machine learning

Maria Kanczewska

12/12/2025, 12:15

Pitch presentation

Refractory complex concentrated alloys (RCCAs) represent a promising class of materials for next-generation high-temperature applications, where conventional Ni-based superalloys and ceramics reach their performance limits. However, their development is hindered by intrinsic brittleness at low temperatures and the vastness of their compositional design space, which exceeds billions of possible...

11. Flue-gas desulfurization

Pavlos Stephanou

12/12/2025, 13:30

Pitch presentation

Scrubbing with seawater is a reliable technology for flue-gas desulfurization in power plants or in marine engines. Then, the removed sulphur is released to the atmosphere, or effluents are released to the sea. In both cases, stringent legal limits for both emissions and effluents are in effect that must be met. There are many input parameters (sulfur content, flow rate, temperature of the...

14. Bacterial Cellulose Polymers for PEM Fuel Cells

Lidia Valdés Llorente (Technical University of Leoben)

12/12/2025, 13:45

Pitch presentation

The utilization of cellulose-producing bacteria in decarbonizing hydrogen technologies, particularly for proton exchange membranes (PEMs) in fuel cells and electrolyzers, is an alternative for more sustainable hydrogen production. Numerous research institutions and universities have investigated the cultivation of these non-pathogenic, naturally occurring bacteria as eco-friendly substitute....

17. Machine Learning-Assisted Wear Behavior Evaluation of P/M Ti-Matrix Hybrid Reinforced Composites

Deniz Uzunsoy (Bursa Teknik Üniversitesi)

12/12/2025, 14:00

Pitch presentation

Titanium is widely used as a matrix material in composites because of its favorable mechanical, physical and chemical properties, yet its cost and limited wear resistance and high-temperature performance restrict some applications. Ceramic-reinforced titanium matrix composites offer a practical route to improve wear behavior.
This study focuses on powder-metallurgy titanium-matrix composites...

15. Machine Learning vs. Constitutive Models: Can Data Replace Equations in Finite Element Analysis?

Özgen Çolak Çakır (Istanbul Technical University)

12/12/2025, 14:15

Pitch presentation

Recent advances in machine learning (ML) have created new opportunities for modeling the mechanical behavior of polymers and composites. Conventional constitutive models require assumptions, parameter calibration, and often struggle with nonlinear, rate-dependent, and temperature-sensitive responses. ML approaches, trained directly on experimental data, offer an alternative, but important...

12. Machine Learning for Adaptive Behavior in Smart Soft Materials

Dr Ayse Cagil Kandemir (TED University)

12/12/2025, 14:30

Pitch presentation

We are developing a new class of soft, adaptive composite materials with tunable stiffness and embedded electromechanical responses. Under controlled mechanical activation, these materials generate complex multimodal signals that are strongly coupled to their internal structure.
Before any robotic finger or human testing, our primary focus is AI for Materials: using machine learning to...