Periodic Reporting for period 1 - M2DESCO (Computational Multi-Models Enabled Design of Safe & Sustainable Multi-Component High-Entropy Coatings)
Reporting period: 2024-01-01 to 2025-06-30
The project is directed at addressing challenges for developing next-generation high-entropy-alloybased multi-component green (free of toxic substances) and sustainable (rare earth free & minimum critical metal elements) coatings with predictable functionalities, performance and life - aiming at increasing wear resistance by 100%, corrosion/oxidation resistance by 50~60 %, and effectively reducing the criticality of coating materials by at least 70%. To achieve these goals, the project is to integrate AI/ML underpinned, highly effective and highly efficient Computational Modelling that is guided by a novel Safe and Sustainability by Design Framework and facilitated by high-throughput characterisations and evaluations, to speed up the material-design to coating-product development process (reducing the development cycle-time by 400~500%), to enable optimal alloy/material design and coating process optimisation for high efficiency and high quality, at the same time, to reduce the overall product manufacturing cost (due to use of the new tooling developed) by 20%. The advancement
of M2DESCO will contribute significantly to combating the loss in EU region caused by corrosion and wear, to the enhancement of the global profile and leadership of the EU material modelling/research community, to strengthening of the innovation capability of the EU coating industry/business, and ultimately to reinforce the PVD EU sector which leads an world-wide market projected to reach $40.97 billion in 2028, thus, rendering great benefit to the wider advanced manufacturing chain, and effectively enhancement of the global competitiveness and the resilience of the EU industry.
of M2DESCO will contribute significantly to combating the loss in EU region caused by corrosion and wear, to the enhancement of the global profile and leadership of the EU material modelling/research community, to strengthening of the innovation capability of the EU coating industry/business, and ultimately to reinforce the PVD EU sector which leads an world-wide market projected to reach $40.97 billion in 2028, thus, rendering great benefit to the wider advanced manufacturing chain, and effectively enhancement of the global competitiveness and the resilience of the EU industry.
The M2DESCO has being running along the three expected technical paths, as responding to the overall Project workflow; namely i) the Modelling approach including the development of individual materials and process models, their enhancement (faster, better performance) through AI/ML tools, and their interconnection (multiscale), ii) the Safe and Sustainable by Design in steps 1-5, encompassing safety/risk, environmental, and social-economical assessments and iii) the Experimental approach, stressing over the need to enhance the performance of specimen/test result production (high throughput testing).
The modelling activities have progressed toward the complex challenge of developing reliable physical methods to compute and predict the intricate structures of high entropy multielemental materials (HEAs), including those synthesized as thin films. The project has implemented models for HEA properties and phase stability using three main approaches: thermodynamic phase equilibrium calculations (CALPHAD), ab-initio ground-state stability via density functional theory (DFT), and molecular dynamics (MD) simulations for large atomic systems. During this initial phase, M2DESCO focused on validating these individual models and establishing an effective multiscale modelling architecture. DFT serves as the primary benchmark for energetic and mechanical properties, enabling the refinement of interatomic potentials used in CALPHAD and MD to extend predictions across longer time and length scales.
Traditional trial-and-error methods are impractical for modelling complex HEAs with more than five elements. To overcome this, the project is developing AI and machine learning tools to identify and select interatomic potentials (MACE). Using cluster expansions, quasi-harmonic calculations, and CPA/KKR methods supported by MACE, the project has begun generating predictive results for HEA properties such as phase stability, elastic modulus, friction, and wetting.
Another key modelling direction involves HEAs produced under non-equilibrium conditions, such as coatings formed via physical vapor deposition (PVD). M2DESCO is exploring advanced techniques like Arc-evaporation and High Power Impulse Magnetron Sputtering (HIPIMS), which are highly relevant to the coating industry. The strategy combines macroscopic computational fluid dynamics with atomistic non-equilibrium Monte Carlo models, previously developed for conventional sputtering. These models are enriched by input from industrial partners and the materials modelling team, with parameters like atomic sticking coefficients helping to bridge processing and material behavior.
On the experimental side, two main approaches are underway. First, test HEA materials and coatings are being produced and validated to generate training datasets for the modelling teams. Two coating families have been selected: hard phases (AlTiCrMoW+N) and corrosion-resistant compositions (CoNiFeMoCr), developed both as bulk targets and coatings. Second, high-throughput testing procedures are being designed and implemented to accelerate development timelines by a factor of five. Two experimental furnaces and two industrial reactors have produced over 100 test specimens, which are now undergoing compositional, mechanical, and microstructural evaluation, with results documented in D3.1. The project is moving toward more complex HEA coating structures and has initiated coating tests on industrial components, with three test runs planned using optimized materials.
Finally, the Safe and Sustainable by Design (SSbD) activities have been successfully launched and aligned with both project and industrial objectives. The full five-step SSbD framework has been designed, covering value chain mapping, benchmark identification, and socio-economic impact analysis. Industrial partners have provided essential data for technical assessments, enabling the construction of safety, environmental, and socio-economic evaluation criteria. AI and ML tools are being applied to enhance safety and risk assessments through SAR models for multicomponent systems, with promising preliminary results. Life cycle assessments have also progressed, focusing on methodology development, data quality evaluation, and gap identification as outlined for this first reporting period.
The modelling activities have progressed toward the complex challenge of developing reliable physical methods to compute and predict the intricate structures of high entropy multielemental materials (HEAs), including those synthesized as thin films. The project has implemented models for HEA properties and phase stability using three main approaches: thermodynamic phase equilibrium calculations (CALPHAD), ab-initio ground-state stability via density functional theory (DFT), and molecular dynamics (MD) simulations for large atomic systems. During this initial phase, M2DESCO focused on validating these individual models and establishing an effective multiscale modelling architecture. DFT serves as the primary benchmark for energetic and mechanical properties, enabling the refinement of interatomic potentials used in CALPHAD and MD to extend predictions across longer time and length scales.
Traditional trial-and-error methods are impractical for modelling complex HEAs with more than five elements. To overcome this, the project is developing AI and machine learning tools to identify and select interatomic potentials (MACE). Using cluster expansions, quasi-harmonic calculations, and CPA/KKR methods supported by MACE, the project has begun generating predictive results for HEA properties such as phase stability, elastic modulus, friction, and wetting.
Another key modelling direction involves HEAs produced under non-equilibrium conditions, such as coatings formed via physical vapor deposition (PVD). M2DESCO is exploring advanced techniques like Arc-evaporation and High Power Impulse Magnetron Sputtering (HIPIMS), which are highly relevant to the coating industry. The strategy combines macroscopic computational fluid dynamics with atomistic non-equilibrium Monte Carlo models, previously developed for conventional sputtering. These models are enriched by input from industrial partners and the materials modelling team, with parameters like atomic sticking coefficients helping to bridge processing and material behavior.
On the experimental side, two main approaches are underway. First, test HEA materials and coatings are being produced and validated to generate training datasets for the modelling teams. Two coating families have been selected: hard phases (AlTiCrMoW+N) and corrosion-resistant compositions (CoNiFeMoCr), developed both as bulk targets and coatings. Second, high-throughput testing procedures are being designed and implemented to accelerate development timelines by a factor of five. Two experimental furnaces and two industrial reactors have produced over 100 test specimens, which are now undergoing compositional, mechanical, and microstructural evaluation, with results documented in D3.1. The project is moving toward more complex HEA coating structures and has initiated coating tests on industrial components, with three test runs planned using optimized materials.
Finally, the Safe and Sustainable by Design (SSbD) activities have been successfully launched and aligned with both project and industrial objectives. The full five-step SSbD framework has been designed, covering value chain mapping, benchmark identification, and socio-economic impact analysis. Industrial partners have provided essential data for technical assessments, enabling the construction of safety, environmental, and socio-economic evaluation criteria. AI and ML tools are being applied to enhance safety and risk assessments through SAR models for multicomponent systems, with promising preliminary results. Life cycle assessments have also progressed, focusing on methodology development, data quality evaluation, and gap identification as outlined for this first reporting period.
A summary of outcomes (still preliminary) beyond the stage of the art are:
1. Complete characterization of the HEA materials and coating value chain, including the identification of HSRs, potential impacts on the environment.
2. Constructed a set of AI/ML based MACE interatomic potentials trainned with DFT calculations to undergo of CALPHAD, MD among other atomistic physical models.
3. A model of multi species deposition was developed based on kinetic montecarlo methods. The model includes calculations of the fluxes from any number of sources/magnetrons to a moving substrate. Existing models were extended to address HiPIMS and ARC evaporation sources containing la high degree of vapour ionization.
4. A family of newly developed AlTiMoW nitride coatings not yet produced and tested.
1. Complete characterization of the HEA materials and coating value chain, including the identification of HSRs, potential impacts on the environment.
2. Constructed a set of AI/ML based MACE interatomic potentials trainned with DFT calculations to undergo of CALPHAD, MD among other atomistic physical models.
3. A model of multi species deposition was developed based on kinetic montecarlo methods. The model includes calculations of the fluxes from any number of sources/magnetrons to a moving substrate. Existing models were extended to address HiPIMS and ARC evaporation sources containing la high degree of vapour ionization.
4. A family of newly developed AlTiMoW nitride coatings not yet produced and tested.