Materials science is at the crossroad between fundamental and applied sciences. Whether enabling clean energy, next-generation computing, or advanced manufacturing, it shapes the tools we use and the systems we build. As our societies undergo rapid digital, environmental, and technological transitions, materials science becomes even more central. It's a space where innovation can respond to practical challenges while aligning with broader social values. In the European context, that means supporting sustainability, openness, and solidarity -while also strengthening competitiveness.This roadmap explores how digital tools-especially simulation, data science, and AI-are transforming materials research, in connection with the twin digital and energy transition. The digital transition refers to the widespread adoption of digital technologies and data-driven methods across sectors, while the green (or energy) transition focuses on shifting toward sustainable, lowcarbon energy systems-together forming what is often called the twin transition, a joint effort to make economies both smarter and more sustainable.The Roadmap outlines both the technical directions and the cultural shifts needed to make this transformation inclusive and effective. Chapters span from atomic-scale simulations to advanced experimentation, from reproducibility to intelligent optimization, and from institutional reform to education for the next generation.Importantly, this isn't a single viewpoint. The document brings together a wide range of voices: researchers from different disciplines, working across length and time scales. It includes early-career scientists and senior experts. Enabled by activities supported by European Cooperation in Science and Technology (COST), it reflects a commitment to gender and geographic diversity. This plurality doesn't just enrich the content-it makes the vision more robust and relevant.We hope this collection serves not just as a guide, but as an invitation to collaborate-across fields, sectors, and borders-as we reimagine the future of materials science in a digital age.

Wind-induced particulate matter (PM) resuspension is an increasingly recognized contributor to urban air pollution. A CFD model of 2D street canyon geometry was developed that can replicate the process of resuspension. Model created the wind speed vs concentration increase due to resuspension relationship and its key properties: threshold wind speed causing resuspension (TWSR) and concentration increase. At least 8.75 m/s in 10 m height inlet wind speed before street canyons was needed to start the resuspension, leading to PM10 concentrations often exceeding 1 μg/m³, with peak values reaching as high as 3 μg/m³. The model can predict the behavior of resuspension well, but it cannot capture all the factors acting in the real environment to match precisely the air quality data. Silt load remains the greatest unknown factor in determining the overall magnitude of resuspension, with observed increases in PM10 concentrations up to 6 μg/m³ in air quality data.

This study investigates the use of deep learning algorithms to predict the discharge coefficient (Cd) of contaminated multi-hole orifice flow meters with circular opening. Datasets (MHO1 and MHO2) were obtained from computational fluid dynamic simulations for two circular multi-hole orifice flow meters of different geometries. To evaluate the performance and generalization capabilities of different models, three distinct scenarios, each involving different dataset configurations and normalization techniques were designed. For each scenario, three deep learning models (feedforward neural networks, convolutional neural network, and recurrent neural network) were implemented and evaluated based on their performance metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). For all three scenarios eight models for each neural network model were developed (FFNN – four models, CNN – two models, RNN – two models). The same structure of models was used across all scenarios to ensure consistency in the evaluation process. Key input parameters include geometrical and flow variables such as β – parameter, contamination thickness, radial distance, Reynolds number, and orifice diameters. Results demonstrate the effectiveness of deep learning in accurately predicting discharge coefficient for different contamination conditions and different geometries. This study showed that deep learning models can be used for prediction of discharge coefficients for multi-hole orifice flow meters of similar geometry, based on data obtained from one orifice flow meter for different contamination parameters.

ABSTRACT Urban air quality assessment in regard to biogenic emissions such as airborne pollen grains (PGs) is indispensable in the urban environment which significantly affects human health, climates, ecosystems, and energy production. Dispersion and deposition of PGs because of resuspension events (ReSE) are investigated using computational fluid dynamics (CFD). The mechanisms in PGs movement are achieved through the Lagrangian formulation-based discrete-phase model (DPM). The observed pollen grains distribution pattern is highly spatially dependent on heterogeneous urban obstacles as it induces modified characteristics of the wind flow field. Wind-urban structure interplays in PGs concentration variation. The simulated results identify the pollen hot-spot zones, where vortical flow features and low wind speed dominate. This approach offers a detailed understanding of the dispersion patterns of airborne PGs in urban environments because of ReSE with high resolution.

- The influence of multi-hole orifice flow meter geometry parameters on the parameters of Newtonian fluid through multi-hole orifice meters was investigated using computational fluid dynamics as well as the effect of contamination in front of the MHO flow meter. The air flow was steady, three-dimensional, and turbulent. Analysed Newtonian fluid was air and physical properties that were considered were density and dynamic viscosity. The numerical method was finite volume method, and standard k-ε turbulence model was used for turbulence modelling. Multi-hole orifice meter with thre e different β parameters 0.5 5, 0.6 and 0.7, was observed and Reynold’s number was 10 5 . The pressure drop and discharge coefficient were analysed. Numerical simulations were performed using commercial software the STAR-CCM+ 2019.2. It was found that increase in 𝛽𝛽 parameter results with the decrease in pressure drop and increase in discharge coefficient. Also, it was found that that the influence of 𝛽𝛽 parameter is much higher when analyzing pressure drop rather than discharge coefficient values. Numerical simulations were also performed to investigate the effect of contaminations in front of the MHO plate with 𝛽𝛽 = 0.5, on the discharge coefficients. It was found that as the contamination angle is increased the discharge coefficient tends to increase.

Drying of textiles in industrial facilities represents an energy-intensive process where a large number of measures for energy and production cost savings can be introduced. Typical measures include the introduction of energy management, waste-heat recovery, process optimization and so on. Drying is a complex process with coupled heat and mass transfer between the heated air and humid textile, where parameters such as the air flow rate, air velocity and its flow regime and textile velocity and water content represent significant influential factors. The distribution of air temperature and density inside the drying section of an industrial stenter frame is analyzed in detail using three-dimensional numerical simulation, where the textile is modeled as a porous medium to analyze moisture diffusion within the textile. Heated air is introduced into a chamber by inlet nozzles and removed by exit nozzles, the distribution of which is based on actual machine configuration. A humid textile is introduced into a section, where temperature and density distribution within the textile are calculated for selected time periods. During the simulation in the Fluent program, models of specific component transport, multiphase air flow, turbulent flow, porosity and evaporation were used. The results represent a valuable data set that provides an in-depth insight into the drying process in the industrial stenter machine.