EventsCoatings 2026: Safe and Sustainable by Design Surface Treatment and Coatings
Published
This submission belongs to the session S5. Application of the SSbD framework on the surface finishing industry of the event Coatings 2026: Safe and Sustainable by Design Surface Treatment and Coatings
Published date
20 Apr, 2026
Academic Editor
author-avatarLuca Magagnin
Citation
Giovanni Bolelli, The CoBRAIN project – an overview, in Proceedings of Coatings 2026: Safe and Sustainable by Design Surface Treatment and Coatings, Athens, 20 April–22 April 2026, MDPI: Basel, Switzerland
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The CoBRAIN project – an overview

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1. University of Modena and Reggio Emilia, Italy
Abstract

The goal of the “CoBRAIN” project is to design novel coating formulations for protection against wear and corrosion using an integrated materials development workflow. We aim to replace existing coatings which employ toxic, carcinogenic substances either as raw materials (e.g. chromium electroplating from CrO3 solutions in water) and/or as constituents of the final product (e.g. cobalt in Stellite alloys and WC-Co hardmetals) and/or employ critical raw materials, such cobalt itself or tungsten. We specifically focus on the novel class of High-Entropy Alloys (HEAs), i.e. nearly equiatomic alloys made of four, five or six elements, employing these alloys either for purely metallic coatings or as matrices for hardmetal coatings, combined with TiC as hard phase.

To explore a vast range of possible compositions, we combined various high-throughput physical modelling techniques to simulate properties such as the equilibrium crystalline phase(s), the stacking-fault energy, the microstructural evolution, and the micromechanical properties of HEAs, and then we trained neural network-based AI models on this extensive dataset. We also developed high-throughput laboratory methods to provide as much input and validation data as possible for these models. An overarching ontology allowed semantic mapping of the dataset into a cloud-based knowledge base to ensure interoperability.

The main results obtained by the CoBRAIN project so far have been:

  • Contributing, to the creation of an improved version of the EMMO ontology for the mapping and storage of data and metadata.
  • Creating an AI-based API to predict the properties of a given HEA composition, or identify candidate HEA compositions based on predefined targets.
  • Setting up an LCPA tool to estimate the costs, environmental impact and health hazards of each novel formulation in comparison to benchmark materials.
  • Developing a sustainable decision support system based on the integration of the AI and LCPA tools, which is now available for demonstration and trial by the project partners and interested stakeholders.
  • Gaining experience on the actual production of HEA-based feedstock powders and of coatings by thermal spraying processes. This is now being scaled-up to industrial case-studies.
Keywords
Sustainable Decision Support System
High-Entropy Alloy
Machine Learning
Coatings
Wear
Corrosion
Thermal Spray
Unveiling Microstructure-Property Relationships for the Design of Sustainable HEA and Cermet Coatings through High-Resolution Nanomechanical 3D High Speed Mapping
High-temperature sliding wear performance of HVOF-sprayed TiC-HEA coatings