10/02/2024
On January 24-25, the Cnr headquarters in Rome hosted the JP AMPEA-DfE & COST-Action EU-MACE workshop
"Digitalization and Automation boost Energy Materials Research". The event was organized by the Joint Programs 'Advanced Materials and Processes for Energy Applications' and 'Digitalization for Energy' of the European Energy Research Alliance-EERA, in collaboration with COST-Action 222123 'European Materials Acceleration Center for Energy- EU-MACE.' Experts, researchers, and innovators from diverse parts of the world gathered to share the latest and most innovative technologies revolutionizing materials science.
The Director of Cnr-Diitetwelcomed guests and presented the research areas of the Department's Institutes that fall under the scientific specificity of EU-MACE.
The workshop's first session focused on supporting the 'Green Digital Transition' through the use of technologies that promote the development of advanced innovative materials. Invited speaker Natalia Konchakova (Helmholtz-Zentrum Hereon, Germany) presented how the VIPCOAT platform could enable teams of researchers to co-design innovative materials and exchange data. The DigiPass platform prioritizes the dual green-digital transition for a circular, sustainable, zero-emission economy. The IAM4EU partnership takes decisive actions to plan the digitization of materials and manage digital product passports.
Hernán Asorey (CIEMAT, Spain) August Wierling (Western Norway University of Applied Sciences, Norway) and focused their presentations on implementing the FAIR (Findable, Accessible, Interoperable, and Reusable) framework in renewable energy sector to promote data dissemination within the scientific community, possibly avoiding large efforts and specialized skills by using csv on the web.
Kourosh Malek (FZ-Jülich, Germany) presentation covered new approaches to metadata management, enhancing cloud-connected laboratories, and developing data ontology for new electrochemical materials to meet the challenge of green hydrogen in the market.
Josua Vieten (ExoMatter, Germany) presented MatterMine platform that aims to develop efficient and cost-effective materials for thermochemistry and energy storage, while IEMAP is designed to accelerate the discovery and selection of sustainable materials for energy technologies through the use of AI technologies and a dedicated database.
Massimo Celino (ENEA, Italy) presented IEMAP platform designed to accelerate the discovery and selection of sustainable materials for energy technologies through the use of AI technologies and a dedicated database.
Anja Bieberle-Hütter (DIFFER, the Netherlands) showed the challenges and chances of using multiscale modeling for electrochemical water splitting in nanostructured materials.
Viktor Mandrolko (Univ. Lorraine, France) presented recent results of molecular dynamics simulations that provided insights into heat transport phenomena across the silica/water interface.
Ainhoa Bustinza (CIC energiGUNE, Spain) illustrated a new approach to materials research to produce batteries that are more efficient and cost-effective. This will require the construction of new laboratory infrastructure and analytical tools, such as high-throughput automated synthesis modules, automated analysis programs capable of handling large amounts of data, and artificial intelligence-assisted experimental planners.
Theodoros Dimopoulos (AIT, Austria) illustrated the results obtained on heterojunction solar cells deposited through combinatorial deposition of thin layers and characterized with automatized I-V measurements and analysis.
Invited speaker Anjuli Szawiola (NRCan, Canada) illustrated how Natural Resources Canada (NRCan) has supported the uptake of the accelerated materials discovery (AMD) approach for clean energy technologies in research facilities around the world. Her talk presented a case study in NRCan’s approach to addressing technology specific challenges in the clean energy technology space for scale-up across the research and innovation continuum via training & knowledge mobilization, as well as connecting in with the broader ecosystem.
Supriya Nandy (VTT, Finland) presented the results obtained from an ML model trained by automatic SEM image acquisition capable of detecting defects produced by additive manufacturing of AISI 316L stainless steel are presented.
Michael Eikerling (FZ-Jülich, Germany) showed how the combined use of theory and computation could accelerate the design and integration of electrocatalyst materials for hydrogen technologies.
Selçuk Yerci (METU, Turkey) discussed how the use of ML significantly reduces the number of experiments required, presenting results obtained on solar cells produced using Bayesian optimization methods.
Mauro Palumbo (UniTO, Italy) illustrated machine learning approaches for the assisted development of metal hydrides. It offers projections for the development of hydrogen storage systems, improved by the PAirwise Difference Regression method.
Filippos Sofos (UTH, Greece) showed examples of Symbolic Regression being applied in situations where theoretical equations were not available, allowing for the construction of a general framework that identifies the parameters influencing material properties and provides a physically consistent expression, limiting the need for experiments and simulations.
The interactive digital posters encouraged further exploration and discussion of the workshop topics.
Per informazioni:
Monica Fabrizio
CNR - Construction Technologies Institute (Cnr-Itc)
monica.fabrizio@cnr.it
Roberta Graci, Cnr-Diitet, email: roberta.graci@cnr.it
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