Make it (net)work! - MLA2S Networking Seminar #11

Thursday 15 Oct 2026, 13:00 → 16:55 Europe/Vienna

11th Network Seminar of the Thematic Platform MLA²S. 
Seminarrooms 2 and 3 (formerly 1 and 2, also known as 3A.1 and 3A.2), PSK 3rd floor

 

To register: click "register" below here. 

13:00 → 13:15 News from MLA2S and Make it (net)work!

Including a short round table news update.

Speakers: Dr Claudius Krause (MBI Vienna (ÖAW)), Claus Trost (Erich Schmid Institute of Materials Science of theAustrian Academy of Sciences), Elisabeth Eder (Austrian Academy of Sciences), Jan Odstrčilík (IMAFO), Kati Heinrich (IGF | ÖAW), Nicki Holighaus (Acoustics Research Institute, Austrian Academy of Sciences)

13:15 → 13:45 From Data-Driven Alloy Design to Characterisation: Machine Learning in Materials Science

The development of advanced materials has traditionally followed a fundamental processing-
structure-properties-performance paradigm. Understanding and optimising these links is
therefore central to designing materials for specific applications. However, the growing
complexity of modern multicomponent alloys has made this task increasingly challenging. The
vast compositional space, coupled with the intricate relationships among chemistry,
processing, microstructure, and deformation mechanisms, makes conventional trial-and-error
approaches inefficient and often requires a prohibitively large number of experiments. Machine
learning offers an opportunity to accelerate the materials development cycle by learning these
complex relationships directly from data. The talk will present how data-driven approaches can
be used to explore and optimise alloy chemistry, predict material properties, and tailor
composition to activate different deformation mechanisms, thereby enhancing performance.
Rather than replacing experiments, machine learning can narrow the enormous design space
and guide experiments towards the most promising alloy compositions, helping to overcome
one of the major bottlenecks in conventional alloy development.
The same data-driven approach can be further extended from design to characterisation.
Advanced electron microscopy techniques such as TEM and 4D- STEM generate rich, spatially
resolved datasets that contain information about microstructural heterogeneity, local
deformation, and strain distributions. While these datasets provide unprecedented insight into
materials, extracting and quantifying such information can be challenging and time-consuming.
The talk will address how machine learning can be integrated with microscopy to accelerate
image analysis, quantify microstructural features and extract information that would otherwise
require extensive manual analysis. The talk will also address the emerging potential of
explainable machine learning to move beyond prediction towards materials understanding. By
identifying the chemical, structural and microstructural features that control model predictions,
explainable machine learning provides a pathway to uncover physically meaningful
relationships rather than treating the model as a black box.
Overall, the talk will demonstrate how machine learning can be embedded across the materials
science paradigm, from alloy design and property prediction to microscopy and mechanistic
understanding, creating a data-driven pathway towards faster materials discovery while
retaining the fundamental goal of materials science.

Speaker: Dr Swati Mahato (Erich Schmid Institute of Materials Science)

13:45 → 14:00 Discussion

14:00 → 14:15 Break to walk a few steps and get some fresh air in.

14:15 → 15:00 An AI Framework for Semantic Processing and Natural Language Reasoning in Reactive Heritage Digital Twins: The ARTEMIS Approach to Integrating and Exploring Cultural Heritage Data

Cultural heritage research increasingly relies on large and heterogeneous collections
of resources, ranging from archaeological catalogues and excavation reports to
scientific analyses, restoration records, images, sensor observations and other forms
of documentation. Artificial Intelligence and Machine Learning offer new possibilities
for processing, connecting and accessing this information. At the same time, Digital
Twins are emerging as a promising framework for bringing together the tangible and
intangible dimensions of cultural heritage: not only the physical characteristics and
changing conditions of heritage objects, monuments and sites, but also the historical
knowledge, cultural practices, interpretations and narratives associated with them.
Developing such systems, however, requires approaches capable of integrating
diverse data while remaining sensitive to domain knowledge, provenance,
interpretability and the essential role of human expertise.
This lecture presents the AI framework developed within ARTEMIS, a project funded
by the European Union dedicated to the creation of Reactive Heritage Digital Twins.
After introducing Heritage Digital Twins, the project, and discussing how Machine
Learning and AI can support the documentation, study, conservation and
management of cultural heritage, the lecture will describe the semantic data
infrastructure underpinning the ARTEMIS ecosystem. Particular attention will be
given to the role of ontologies and Knowledge Graphs in integrating diverse data
while explicitly representing their meaning, relationships, and provenance.
The central part of the lecture will present two complementary AI contributions. The
first is an AI semantic annotation pipeline designed to transform heterogeneous
and partly unstructured cultural heritage documentation into structured, ontology-
aligned knowledge. The pipeline combines Large Language Models, semantic
retrieval and ontology-based entity linking to identify relevant concepts and relations,
associate them with formal cultural heritage ontologies and generate RDF
statements for Knowledge Graph population. A short demonstration will illustrate this
process using related documentation.
The second contribution is the Artemisia reasoning engine, which operates in the
opposite but complementary direction: rather than using AI to populate the
Knowledge Graph, it uses the graph and its ontological structure as a foundation for
AI-based querying and reasoning. Through the Artemisia chatbot, users can
formulate questions in natural language and explore interconnected cultural heritage
data without needing to understand the underlying ontologies, graph structure or
SPARQL query language. The Knowledge Graph provides the system with explicit
semantic context and traceable evidence, helping it identify relevant resources,
traverse meaningful relationships and generate grounded responses. A live
showcase will demonstrate how this approach facilitates access to distributed and
semantically diverse information.
More broadly, the lecture will also address the importance of selecting and
configuring the “right” AI for specialised domains. The ARTEMIS approach aims to
minimise hallucinations, protect data sovereignty and make AI outputs more
transparent, traceable, and verifiable. This approach also responds to the principles
of accountability, transparency and human oversight promoted by the European AI
Act.

Speakers: Aida Himmiche (Fondazione PIN, University of Pisa), Mariana Somenzi (ARIADNE RI, University of Pisa)

15:00 → 15:30 Discussion

15:30 → 16:30 Networking with refreshments / further discussions